The Aisot Technologies Seed Raise and Your Website or App: A Guide for Professional Services Firms in Switzerland

Direct answer: Aisot Technologies, a spin-off from ETH Zurich, closed a CHF 2 million seed extension in August 2026, and it matters to Swiss professional services firms because it confirms that serious, research-grade AI teams are now being funded specifically to build applied AI agents rather than research demos. If you run a legal, tax, consulting, audit, or advisory practice in Switzerland, this is a signal that your clients will start expecting AI-agent-level responsiveness from your website and internal tools, not just a contact form.

According to Swiss startup news reporting from August 2026, Aisot Technologies — a company spun out of ETH Zurich — raised a CHF 2 million seed extension round. This is a modest, specific data point, and we are not going to inflate it into something it is not: the report does not specify use of funds beyond growth, nor does it name enterprise clients, nor does it give a valuation. What it does confirm is a pattern that has been building across the Swiss AI ecosystem for the past two years — technical teams coming out of institutions like ETH Zurich are increasingly choosing to commercialize applied AI systems (agents, automation, decision-support tools) rather than staying in pure research, and investors are willing to fund extension rounds even at the seed stage to keep that momentum going. For a professional services firm — the kind of business built on expertise, trust, and responsiveness — that pattern is worth paying attention to, because it changes what "modern" looks like on your own website and in your own client-facing tools.

What the Aisot Seed Extension Actually Signals

A seed extension is a specific kind of funding event. It is not a first check into an idea; it is additional capital going into a company that has already proven enough to convince its earliest backers to come back for more, or to bring in new investors on the strength of traction so far. When that company is an ETH Zurich spin-off working in applied AI, the signal compounds: Switzerland's research infrastructure is producing commercially viable AI companies at a pace that is starting to show up in seed-stage funding data, not just in academic papers.

For professional services firms — law practices, tax and audit firms, management consultancies, wealth advisory boutiques — the direct takeaway is not "go build what Aisot builds." It is narrower and more useful: the market you sell into is absorbing AI agents into its expectations faster than most firms' websites and client portals have adapted. If a well-funded, technically credible AI company can raise capital in Switzerland's relatively conservative funding environment on the strength of applied AI agent work, that is evidence the demand side — enterprise and professional buyers — is ready to pay for and use these systems, not just read about them.

Why This Is Different From Generic "AI Hype"

Switzerland's funding environment tends to be more measured than markets like the US or UK. A CHF 2 million seed extension here, tied to an ETH Zurich pedigree, carries more signal-to-noise than a similarly sized round elsewhere, because Swiss investors and the ETH ecosystem specifically are known for technical due diligence rather than momentum investing. That is the honest reading of this data point — not a market explosion, but a credible, technically vetted confirmation that applied AI agent work is fundable and, by extension, buildable at production quality right now.

It also matters that this was an extension round rather than a fresh seed check. Extension rounds happen when existing or new investors want more exposure to a company after seeing real progress, not when a founder is simply pitching a slide deck for the first time. That distinction is easy to miss in a quick headline, but it is the difference between "someone believes this idea could work" and "someone has watched this idea work well enough to put more money behind it." For professional services firms trying to gauge how seriously to take AI agent claims from vendors and competitors alike, that distinction is a useful filter: extension-stage funding in this space is a stronger signal than first-round enthusiasm.

None of this means every AI vendor knocking on a Swiss law firm's door is credible, or that every claim about "AI transformation" deserves belief. It means the underlying category — applied AI agents built by technically serious teams — has cleared a bar that matters to cautious, diligence-driven investors. Firms evaluating their own AI investments can use the same standard: look for evidence of narrow, working systems with a track record, not broad promises.

Why This Matters Specifically to Professional Services Firms in Switzerland

Professional services firms sell judgment, but they deliver it through processes: intake, document review, scheduling, status updates, billing, compliance checks. Historically, the website and app layer of a professional services firm has been the least "smart" part of the business — a marketing shell in front of expertise that lives entirely in people's heads and in email threads.

That gap is now visible to clients in a way it wasn't three years ago. Clients of Swiss law firms, tax advisors, and consultancies increasingly interact with AI agents elsewhere — booking systems that triage requests automatically, portals that answer status questions without a human, document intake that pre-sorts and flags issues before a person looks at them. When an ETH Zurich-backed company can raise capital specifically to build more of this, it tells you the tooling to do it well is maturing quickly, and the cost of staying behind is rising just as quickly.

The Trust Dimension in Switzerland Specifically

Swiss professional services carry a particular reputation for precision, discretion, and reliability. That reputation is an asset, but it can also become a liability if it hardens into "we do things the traditional way" long after clients have started expecting more. A firm that adopts AI agents thoughtfully — for intake triage, document summarization, scheduling, or first-pass client questions — while keeping human judgment at every point that matters, actually reinforces the Swiss reputation for precision. It shows the firm is applying the same rigor to its own operations that it applies to client work. A firm that ignores this shift risks looking slower and less rigorous than its own market positioning claims.

There is also a competitive dynamic specific to Switzerland's professional services market that is worth naming directly. Many Swiss firms compete internationally for corporate and institutional clients who are simultaneously evaluating advisors in London, Singapore, or New York — markets where AI-assisted service delivery is moving faster in visible, client-facing ways. A Swiss firm's traditional advantages — discretion, regulatory expertise, stability — do not automatically offset a first impression that feels a full technology generation behind. The firms that will hold their competitive position are the ones that pair Swiss rigor with contemporary delivery, rather than treating the two as separate concerns.

It is equally important to be precise about what this does not mean. It does not mean stripping the personal, relationship-driven character out of Swiss professional services, and it does not mean exposing clients to unreviewed AI output on matters of legal, tax, or financial consequence. The firms getting this right are using AI agents to handle the surrounding logistics — the scheduling, the document sorting, the "where is my case at" questions — precisely so that the relationship-driven, judgment-heavy parts of the work get more attention from qualified people, not less.

What Changes in Practice for Your Website or App

This is the part that matters more than the funding news itself. If applied AI agents are becoming a normal part of how professional services get delivered, three things change for the digital layer of your firm.

First, your website stops being a static brochure and starts being an entry point into a working system. A contact form that dumps into an inbox is no longer a neutral choice — it is a visible gap compared to firms whose intake is handled by an agent that asks clarifying questions, routes the inquiry to the right team, and gives the prospective client an immediate sense of what happens next. This is exactly the kind of gap our 10 Best UI/UX Website Examples (2026) roundup keeps surfacing: the firms that look most credible today are the ones whose first digital interaction feels responsive and specific, not generic.

Second, the internal tools your team uses to serve clients become a competitive variable, not just an operating cost. Document review, status tracking, billing preparation, and compliance checks are all processes that AI agents can now assist with meaningfully, provided they are built with proper guardrails, audit trails, and human sign-off at decision points. This is squarely the domain of AI Agents & Automation — not replacing your professionals' judgment, but removing the repetitive, rule-bound steps that surround it, so the humans spend their time on the parts of the work that actually require expertise.

Third, how you present information on your own site needs to hold up to more scrutiny. As client expectations shift toward interactive, responsive experiences, static information blocks and dense paragraphs start to read as dated. Structured presentation — clear cards for service lines, distinct visual treatment for different client segments, scannable comparisons — becomes more important, not less. Our piece on Card-Based UI Design: When Cards Work and When They Don't is a useful reference here, because professional services firms are especially prone to overusing cards for content that would be clearer as a table or a narrative section, and the wrong choice undermines the same trust an AI-agent-equipped site is trying to build.

Where the Analogy to Product-Led Businesses Holds

It is worth noting that this shift is not unique to professional services — it mirrors what direct-to-consumer and product businesses went through when they moved from static storefronts to fully instrumented tech stacks with automation baked into every step. Our guide on Building a D2C Ecommerce Brand's Tech Stack From Scratch documents that transition in a different vertical, but the underlying lesson transfers directly: the businesses that treated their digital layer as infrastructure, not decoration, were the ones that scaled service quality without scaling headcount at the same rate. Professional services firms in Switzerland are at the early edge of the same transition now.

What to Do About It

You do not need to become an AI company to benefit from this shift, and you should be skeptical of any vendor who tells you otherwise. The practical path is narrower and more disciplined than that.

It also helps to be honest about what a firm cannot do internally. Most professional services firms do not have engineering teams on staff, and building AI agents is a specialized discipline distinct from the firm's own expertise in law, tax, audit, or advisory work. That is not a reason to avoid the work — it is a reason to bring in a partner who has built these systems before, scope the project tightly, and insist on measurable outcomes rather than vague promises about "AI transformation." A firm that tries to build this in-house without the right experience often ends up with a system that looks impressive in a demo but breaks down against the messy reality of real client documents and real edge cases.

The good news is that this work does not require a large, drawn-out program to show results. A tightly scoped first project — fixing intake, or automating one document-heavy step — can be delivered and measured within a single quarter, which gives leadership a concrete result to evaluate before committing to anything larger. Start by mapping the repetitive, rule-bound parts of your client-facing process — intake questions, document requests, scheduling, routine status updates — and identify which of those could be handled by a well-scoped AI agent with human oversight, rather than trying to automate everything at once. Then audit your website against what a prospective client actually experiences in the first two minutes: is the first interaction a form, or does it feel like the firm is already listening? Finally, treat your internal tooling with the same seriousness as your client-facing site — a firm that has an excellent public brand but still runs client status updates over scattered email threads is carrying operational risk that eventually surfaces as a trust problem.

A Realistic Sequencing

Most firms should not attempt a full agent rollout in one project. A sensible sequence is: first, fix the public-facing entry point (site UX and intake), because that is what prospective clients see immediately; second, introduce a single well-defined internal agent — document intake triage is a common starting point for professional services — and measure it against a clear accuracy and time-saved bar before expanding; third, extend automation to adjacent processes only once the first agent has a track record inside the firm. Firms that skip straight to "automate everything" tend to produce systems nobody trusts, which defeats the purpose entirely.

This sequencing matters more than it might seem at first glance. A firm that jumps straight into a complex, multi-step automation project without first proving a narrow version of it tends to run into two predictable problems. The first is scope creep: without a working reference point, it is easy to keep adding "just one more integration" until the project outgrows its budget and timeline. The second is trust erosion inside the firm itself — if the first thing staff see from an AI agent is an ambitious system with rough edges, they will be far more skeptical of the next, better-built one than if they had first seen a small, reliable win.

There is also a practical reason to start with the public-facing site rather than internal tooling: it is usually lower risk and faster to validate. A redesigned intake flow can be tested with real prospective clients within weeks and adjusted based on direct feedback, while an internal agent touching case files or financial data requires more careful integration work and testing before it can be trusted with live client data. Sequencing the lower-risk, faster-feedback project first also builds internal confidence and budget justification for the higher-risk, higher-value work that follows.

Pricing Context: What This Work Typically Falls Under

The scope of work here varies a lot by firm size and how much of the process you want touched. As a rough guide to how this kind of engagement typically maps to service tiers:

Tier	Typical scope for a professional services firm
Essential ($1,000)	Website UX refresh focused on intake and first-interaction clarity, no backend agent work
Growth ($2,000)	Website refresh plus a single scoped AI agent (e.g., intake triage or scheduling automation) integrated with existing tools
Enterprise ($4,000+)	Full client journey redesign with multiple integrated agents across intake, document handling, and status communication, plus audit trails

These are the real tiers Scult works within, and where a given firm lands depends on how many processes need touching and how much existing infrastructure has to be integrated against.

Key Takeaways

• Aisot Technologies' CHF 2 million seed extension, reported by Swiss startup news in August 2026, confirms that applied AI agent companies are being funded seriously in Switzerland's technical ecosystem, not just talked about.
• This is a demand-side signal for professional services firms: clients are increasingly exposed to AI-agent-level responsiveness elsewhere and will notice its absence in your firm's own website and tools.
• The highest-leverage first move is usually fixing the public intake experience, since that is the first thing a prospective client actually judges.
• Internal automation should start with one well-scoped process — document intake or scheduling are common starting points — with human sign-off retained at decision points.
• Presentation matters as much as capability: structured, scannable UI (and avoiding overused card layouts where they don't fit) reinforces rather than undercuts the trust a Swiss professional services brand depends on.
• Treat this as a sequenced investment, not a single project — public-facing UX first, one internal agent second, broader automation only after that agent has proven itself.

If you want help figuring out where a Swiss professional services firm should start with any of this, book a meeting with our team.

Frequently Asked Questions

What exactly did Aisot Technologies announce in August 2026?

Aisot Technologies, a spin-off from ETH Zurich, raised a CHF 2 million seed extension round, according to Swiss startup news reporting from that month. No further financial details, named clients, or valuation figures were disclosed in that reporting.

Is Aisot Technologies affiliated with Scult?

No. Aisot Technologies is an independent ETH Zurich spin-off referenced here purely as a market signal about applied AI funding in Switzerland. Scult is not affiliated with Aisot and has no involvement in its funding or products.

Why should a law firm or consultancy care about a seed funding round in a different company?

The specific company matters less than what the funding event reveals about the broader market: investors are willing to fund applied AI agent companies at the seed stage in Switzerland, which means the technology and expectations around it are maturing quickly. Professional services firms compete for the same clients who are being exposed to that technology elsewhere.

What is an "AI agent" in the context of a professional services website?

In this context, an AI agent is a system that can carry out a bounded task — such as triaging an intake request, answering a routine status question, or pre-sorting documents — with defined rules and human oversight, rather than a static form or a purely informational chatbot. It acts, within limits, rather than just displaying information.

Does adopting AI agents mean replacing lawyers, consultants, or advisors?

No. The realistic and defensible use of AI agents in professional services is to remove repetitive, rule-bound steps around the expert's work — intake, scheduling, document sorting — while keeping judgment, advice, and decisions with the qualified human professional.

How does this trend specifically affect firms based in Switzerland versus elsewhere?

Switzerland's funding and business culture is comparatively conservative and technically rigorous, so a seed extension tied to an ETH Zurich pedigree carries more credibility signal than a similar round in a more hype-driven market. For Swiss firms, that means the underlying technology is being vetted seriously before it reaches your clients' expectations.

What is the first thing a professional services firm should fix on its website?

The intake experience is usually the highest-leverage starting point, because it is the first thing a prospective client interacts with directly. A generic contact form that dumps into an inbox increasingly reads as behind the times compared to a more responsive, guided intake flow.

How long does a website intake and UX refresh typically take?

For a professional services firm, a focused intake and UX refresh is often achievable within a few weeks, depending on how much of the existing site needs restructuring versus how much is a targeted redesign of the entry point. Scope and existing infrastructure are the main variables.

What does an AI agent for document intake actually do?

A document intake agent typically reviews incoming files against defined rules — checking for completeness, flagging missing information, and routing documents to the right internal queue — before a person reviews them. It reduces the manual sorting step without making judgment calls that belong to a qualified professional.

Is it risky for a Swiss professional services firm to use AI agents given data protection rules?

Any AI agent handling client documents or personal data needs to be built with Swiss data protection requirements in mind, including where data is processed and stored and how access is logged. This is a reason to work with implementers who understand these constraints rather than a reason to avoid automation altogether.

How much does adding an AI agent to a client-facing process typically cost?

Scope varies, but a single well-defined agent integrated into existing tools — such as intake triage or scheduling — typically falls into a mid-tier engagement, while a full client journey redesign with multiple agents is a larger, enterprise-level scope. The pricing table earlier in this article gives a rough sense of how that maps to tiers.

What is the difference between the Essential, Growth, and Enterprise tiers for this kind of work?

Essential ($1,000) generally covers a UX-focused refresh without agent work, Growth ($2,000) adds a single scoped AI agent integrated with existing tools, and Enterprise ($4,000+) covers a fuller client journey redesign with multiple integrated agents and audit trails. The right tier depends on how many processes need to change and how much existing infrastructure has to be connected.

Should a small consulting practice attempt full automation right away?

No. A sensible approach is to sequence the work — fix the public-facing intake experience first, introduce one internal agent and prove it works, then expand. Attempting to automate everything in one project tends to produce systems that staff don't trust and stop using.

What kind of processes are good first candidates for an AI agent in a professional services firm?

Document intake triage and scheduling coordination are common first candidates because they are rule-bound, high-volume, and low-risk if a human still reviews the output before anything client-facing happens. They also produce measurable time savings quickly, which helps justify further investment.

How do I know if an AI agent is actually working well once it's live?

Track a clear before-and-after metric such as time spent on the manual version of the task, error or rework rate, and how often staff override or correct the agent's output. If overrides are frequent, the agent's scope or rules likely need tightening before it's expanded to other processes.

Does this trend apply to accounting and audit firms as much as law firms?

Yes. Accounting and audit work involves a similar mix of expert judgment and repetitive, rule-bound processing — document review, reconciliation checks, client status updates — that makes it well suited to the same intake-first, single-agent-second approach described here.

What happens if a professional services firm ignores this shift entirely?

The immediate risk is not obsolescence but a widening perception gap: clients who experience faster, more responsive AI-assisted service elsewhere will start to notice when a firm's own intake and communication feel slower and less structured by comparison, even if the underlying expertise is excellent.

Is a chatbot the same thing as an AI agent for this purpose?

Not quite. A basic chatbot typically only answers questions from a script or a knowledge base, while an AI agent as discussed here can take bounded actions — routing a request, flagging a document, updating a status — within defined rules, which is a meaningfully different and more useful capability for operational processes.

How does card-based design relate to any of this?

As firms modernize their public-facing sites alongside internal automation, presentation choices matter more, not less — poorly chosen card layouts for content that needs more context or nuance can undercut the same trust an AI-agent-equipped site is trying to build. Choosing the right layout for each type of content is part of making the modernization feel coherent.

What is the risk of using AI agents without human oversight in a professional services context?

Removing human sign-off from decisions that require professional judgment creates both a quality risk and, in regulated fields like law and tax advisory, a potential compliance risk. The defensible model keeps agents scoped to preparation and triage, with a qualified person making or approving the substantive decision.

Can an existing website be upgraded incrementally, or does it need a full rebuild?

Most professional services sites can be upgraded incrementally — starting with the intake flow, then extending into other areas — rather than requiring a full rebuild. A full rebuild is usually only necessary if the underlying platform can't support the integrations an agent needs.

What technical requirements does an AI agent integration usually have?

It typically needs access to the relevant internal systems (email, document storage, scheduling tools) through defined integrations, plus logging so actions can be reviewed and audited. The specific requirements depend on which process is being automated and what tools the firm already uses.

How does this trend connect to Switzerland's broader AI ecosystem beyond ETH Zurich?

ETH Zurich is one of several strong technical institutions feeding Switzerland's AI ecosystem, and seed-stage activity like Aisot's extension reflects growing investor confidence across that broader base, not an isolated event. It is part of a pattern of applied AI commercialization coming out of Swiss research institutions.

Will client expectations around AI agents keep rising after 2026?

Based on the current pattern of funding and adoption, it is reasonable to expect client expectations to keep rising rather than plateau, since more funded companies are building agent-based tools that clients encounter across industries. Firms that build a foundation now will have an easier time keeping pace than those starting from zero later.

What is the biggest mistake firms make when adopting AI agents?

The most common mistake is trying to automate an entire process end-to-end before proving a narrower, well-scoped version works reliably. Starting too broad makes it hard to isolate what's failing and erodes staff trust in the system.

How does an AI agent handle a client asking a sensitive or unusual question?

A well-designed agent is scoped to recognize when a question falls outside its defined rules and routes it to a human, rather than attempting to answer everything itself. This routing boundary is one of the most important design decisions in building the agent.

Does adopting AI agents require hiring in-house technical staff?

Not necessarily. Many professional services firms work with an implementation partner to design, build, and maintain the agent and its integrations, rather than building in-house AI capability from scratch, which is often not a good use of a firm's specialization.

What is the role of audit trails in AI agent deployments for professional services?

An audit trail records what the agent did, when, and on what basis, which matters both for internal quality control and, in regulated fields, for demonstrating that appropriate oversight was in place. It should be considered a required part of the build, not an optional add-on.

How should a firm measure ROI on an AI agent investment?

The clearest measures are time saved on the automated task, reduction in manual errors or rework, and improvement in how quickly clients receive a first response. These should be tracked from before the agent is introduced so the comparison is meaningful.

Are there professional services firms in Switzerland already using AI agents this way?

The specific companies and their deployments are generally not publicly disclosed at this level of detail, and it would be inaccurate to name specific firms without a verified source. What is publicly evident is the broader funding and adoption pattern described in this article.

What is the difference between automating a process and simply digitizing it?

Digitizing a process just moves it online without changing how it works — a PDF intake form is still a form. Automating it with an AI agent changes the workflow itself, for example by having the system pre-sort or route the submission based on its content.

How does this affect wealth management and financial advisory firms specifically?

Wealth and financial advisory firms handle a similar mix of relationship-driven judgment and process-heavy compliance work, making them well suited to the same pattern: modernize the client-facing entry point first, then introduce a scoped internal agent for a specific compliance or intake process.

What should be in scope for a first conversation with an implementation partner?

A useful first conversation covers which processes are most repetitive and rule-bound today, what tools and data the firm already uses, and what a reasonable first agent's boundaries should be. Coming in with a specific process in mind, rather than a vague automation goal, tends to produce a better scoped project.

Is it worth redesigning the website before or after introducing an internal AI agent?

Sequencing the public-facing website first is usually more effective, since it's what prospective clients see immediately and typically requires less integration complexity than an internal agent. It also gives the firm early experience with a smaller-scope project before tackling a more technical internal build.

How does Scult typically start this kind of engagement?

Scult typically starts by mapping the firm's current intake and client communication flow, identifying the highest-leverage first fix, and scoping a project against one of the pricing tiers described above based on that assessment.

What happens to email-based client communication as agents get introduced?

Email doesn't need to disappear, but routine status questions and scheduling can be shifted to a more structured, agent-assisted channel, freeing email for the substantive back-and-forth that actually requires a person's judgment.

Can AI agents help with multilingual client communication in Switzerland?

Yes, this is a practical advantage in a multilingual market like Switzerland — a well-built agent can handle initial triage and routine communication in multiple languages consistently, though nuanced or sensitive matters should still route to a person fluent in the client's preferred language.

What is the realistic timeline to see results from a first AI agent deployment?

Most firms can expect to see measurable time savings on the automated task within the first month or two of a well-scoped agent going live, provided the process it handles was clearly defined before the build started.

How does this seed funding news relate to AI adoption trends more broadly in 2026?

It's one specific, verifiable data point within a broader 2026 pattern of applied AI agent companies attracting funding across multiple markets, which is why it's referenced here as a grounded example rather than treated as an isolated curiosity.

Should a firm be worried about over-automating and losing the personal touch clients value?

That risk is real if automation is applied without discipline, which is why the recommended approach keeps agents scoped to preparation and routine tasks while preserving direct human interaction for advice, judgment, and relationship-building moments.

What's the difference between a website redesign and a full client journey redesign?

A website redesign typically focuses on the public-facing site and intake, while a full client journey redesign extends further into internal tools and multiple touchpoints across the client relationship, which is why it sits at a higher investment tier.

How does card-based UI differ from a table for presenting service tiers, like the pricing table above?

Cards work well for a small number of visually distinct options a reader is meant to compare at a glance, while a table works better when the reader needs to compare specific attributes side by side, which is why pricing tiers are often presented as a table rather than as cards.

What ongoing maintenance does an AI agent need after launch?

An agent needs periodic review of its accuracy and override rate, updates when the underlying process or rules change, and monitoring to catch any drift in performance, similar to how any operational software needs upkeep rather than a one-time build.

Is this relevant to firms outside Switzerland as well?

The underlying pattern — funded, credible AI agent companies raising capital and rising client expectations as a result — is not unique to Switzerland, though the specific signal referenced here comes from the Swiss market via Swiss startup news reporting in August 2026.

What's a reasonable first question to ask internally before starting this kind of project?

Ask which single process, if automated with appropriate oversight, would save the most staff time or improve the client's first impression the most — that answer usually points directly to the right starting scope.

Does this require a firm to change its case or client management software?

Not necessarily. Many agent integrations work alongside existing case or client management software through defined integrations rather than requiring a wholesale platform change, though very outdated systems may need evaluation first.

How does Scult ensure client data stays secure when building these systems?

Scult scopes integrations with data handling and access logging built in from the start, and treats compliance requirements as part of the initial design conversation rather than an afterthought, particularly for firms handling sensitive client or financial information.

What's the risk of choosing the cheapest possible implementation for an AI agent?

An under-scoped agent with unclear boundaries or no audit trail tends to produce inconsistent results and staff distrust, which often costs more in rework and lost confidence than a properly scoped project would have in the first place.

How do I bring this topic up with partners or leadership at a professional services firm?

Framing it around a specific, narrow first step — such as fixing the intake flow or automating one document review task — tends to get more traction than a broad pitch about "AI transformation," since it gives leadership something concrete to evaluate.

What's the best way to get a clearer sense of what this would look like for our specific firm?

The most direct way is to walk through your current intake and internal process with a team that has built these systems before, so the recommendation is scoped to your actual workflow rather than a generic template — which is exactly what a conversation with Scult's team is for.