Generic AI is good at everything except the thing you actually need, your clients, your data, your process, your last six months of decisions. We build assistants that sit inside Slack, Teams, or your own tools, connected to the systems your team already works in, so the answer comes back with your context in it.
Live in 3–4 weeks · Runs on your data, in your tools, owned by you
account-questions
Slack · internal assistant
what's the status on the Marlow & Co renewal?
Contract renews 14 Sept · last invoice paid 3 Aug · one open support ticket (#2291, waiting on us) · account owner is Priya.
Sources: CRM, billing, helpdeskclosing #2291 now, then I'll call Priya about the renewal
Trusted by teams in the US, UK, and India
Siddhraj
Unoloft
Kofeko
3nStar
Veda
Cerata
Shubham
Consultup IndiaSiddhrajUnoloftKofeko3nStarVedaCerataShubhamConsultup IndiaWe're not going to tell you the assistant you're already paying for is bad. ChatGPT, Claude, Copilot, and Gemini are genuinely excellent, and if your team is getting value from them, that's money well spent.
But there's a pattern worth naming: across the industry, enterprise assistant seats get bought widely and used thinly. Licences go out to the whole company and a fraction of people open them weekly. Analysts have been unusually blunt about it, value from these deployments is not something the licence guarantees.
The reason isn't the model. It's that a generic assistant doesn't know anything about your business until someone types it in, every single time.
Ask it about a client and it knows nothing, not the contract terms, not the open tickets, not what was agreed on the call in March. Your people become the integration layer, pasting background in before every useful question. That’s the friction that kills adoption.
It can draft an email; it can’t check whether the invoice was paid. It can summarise a document you upload; it can’t find the document.
Every conversation starts from zero. Nothing accumulates.
No sense of who’s asking, what they’re allowed to see, or what their job actually is.
An assistant with your systems already connected, CRM, project tool, billing, documents, support desk, that knows who's asking and what they're permitted to see, remembers your organisation's context, and lives where your team already works instead of in another tab.
The gap in 2026 isn't access to a good model. Everyone has one. The gap is that the good model doesn't know anything about you, and nobody has time to keep telling it.
Ask about any client and get the real picture, contract terms, open tickets, last invoice, recent activity, who owns it, assembled from your CRM, billing, and support desk instead of from four browser tabs.
“What’s blocked this week?” “Which projects are over budget?” “Who’s unassigned?” Answered from your project and time-tracking data, on demand, without anyone building a report.
Call prep in one message: history, open deals, past objections, what was promised. Follow-ups drafted with the context already in them. Notes filed back where they belong.
Proposals, briefs, and reports drafted against your own past work, your templates, and your tone, so the first draft starts from your standards rather than from a blank page.
“How do I request time off?” “What’s our refund policy?” “Where’s the template for this?” Answered from your own documentation, with the source cited so people can verify it.
Questions asked in plain language against your database or warehouse, returning a real answer with the query shown, so an analyst isn’t the bottleneck on every routine number.
Most clients start with one, aimed at whatever question their team asks each other most often. That question is usually obvious once you look at a week of Slack.
The fastest way to create a problem is to connect an assistant to everything and let it answer everyone identically. Someone asks an innocent question and gets back a number from a document they were never meant to open.
Gets the complete answer
Gets a partial answer, restricted parts marked as not visible to them
Told it can't answer that for them
The assistant checks who’s asking and respects the access they already have in the underlying system. If a person can’t open the document, the assistant won’t summarise it for them.
We agree what’s in and what’s out before building, per source and per group. HR and finance are usually a separate assistant with a separate audience, not a filter on a shared one.
Every response points at its source so a person can verify it. An assistant that can’t show its working gets trusted for exactly as long as it takes to be wrong once.
What was asked, what was retrieved, what came back. If a question ever needs auditing, the record exists.
We use API tiers with training disabled, or self-hosted models where the data can’t leave your environment.
Say no to a source rather than filter it later. A narrower assistant that people trust completely beats a broader one that legal is nervous about.
An assistant in another tab is an assistant people forget. The channel matters as much as the capability.
A DM, or a bot in the channels where the work already happens.
Same, for Microsoft-based teams.
Embedded where your staff already work.
Context-aware help alongside whatever they’re looking at.
For people who won’t adopt a new interface, and there’s always someone.
Tool integrations are increasingly built on the Model Context Protocol, which is becoming the standard way assistants connect to business systems. Where a system supports it, we use it, fewer bespoke connectors to maintain, and less work if you change models later.
We look at what your team asks each other repeatedly, and how long each answer currently takes to assemble. The best first assistant is almost always obvious from a week of Slack history. You get that shortlist whether or not you hire us.
Which systems it reads, who can ask what, what’s deliberately excluded. Agreed in writing before anything is connected.
Integrations, retrieval, permission checks, and the interface it lives in. Tested against real questions from your real data.
Live to five or ten people first. We watch what they ask, what it gets wrong, and what they stop asking it. Bad answers at ten users are cheap. The same answers at a hundred users lose you the rollout permanently.
Sources added, gaps closed, and the questions it can’t answer tracked as a coverage list rather than ignored. Monthly review of what got asked and what failed. Models change, your data changes, and an assistant nobody tunes gets quietly worse.
The terms get used interchangeably by vendors, which is convenient for them and expensive for you. Here's the distinction that matters when you're deciding what to build.
A chatbot answers.
Ask a question, get an answer from a defined body of content. One job, done reliably, usually customer-facing.
RAG-powered chatbotsAn assistant prepares.
Multi-turn, conversational, connected to your systems, working alongside a person who reviews and acts. It drafts the email, assembles the brief, pulls the numbers, and a human decides what to do. Internal, usually.
This pageAn agent acts.
It completes the task end to end without waiting for a person: reads the invoice, validates it, posts it, files it. More capable, more expensive, and it needs far more testing, because a mistake becomes an action rather than a suggestion.
AI agent developmentThe practical difference is where the human sits. With an assistant, the person is the last step. With an agent, the person is the exception handler. That one design choice drives cost, build time, testing burden, and risk more than any other decision you'll make.
Honest answer: we start most clients on an assistant. It's cheaper, it's safe by construction because a person approves everything, and after a month of use you can see exactly which requests are so repetitive and so low-judgement that they should become an agent. That's a far better basis for the decision than guessing upfront, and it's why the assistant usually pays for the agent.
All three run on the same underlying layer, model selection, retrieval, evaluation, guardrails. Generative AI & Custom LLMs covers that engineering foundation directly.
Trust is asymmetric. One confidently wrong answer in week one costs more than fifty good ones earn. Pilot small, cite sources, and let it say it doesn’t know.
A brilliant assistant behind a link on the intranet gets used twice. The channel isn’t a detail, it’s most of whether this works.
Teams have one or two questions they actually want answered. If those two aren’t covered, general capability doesn’t compensate. Find the real questions first.
Sources go stale, systems change, coverage gaps never get closed, and it degrades until people stop bothering. This is the part most vendors skip and it’s why we scope the monthly plan from day one.
See what the monthly plan actually covers — the coverage-gap and source-freshness work that keeps an assistant from quietly degrading.
Book a 30-minute call. We'll find the question your team assembles by hand most often, tell you what it would take to answer it automatically, and give you a fixed price. If a tool you already pay for would do it, we'll tell you that instead.
