A feature list says what a product can do. This says what it is like to run a business on it — six different kinds of company, from the first week to the first year, and why the second month is better than the first.
Every client has its quirks, and most of them live in two senior technicians' heads. Tickets pile up overnight, the same questions get answered from scratch by whoever picks them up, and a new hire takes months to become useful. InsideAI turns the firm's own documentation, its incoming tickets and the manufacturers' manuals into one place anybody can ask — with each technician's email and documents reached through their own sign-in, never a shared login.
Thirty technicians. A few hundred client businesses. One ticket queue that never sleeps.
The knowledge base is indexed and new tickets are kept in step with it. A technician asks what the firewall setup is at a client in their own words and gets the answer from the firm's own notes rather than from general knowledge. Each technician connects their own email and documents as themselves, so what it finds there is exactly what that technician could already open — and the conversations about a sensitive account can live in a shared project that only the people on that account are invited to.
Set up with us, a scheduled run pulls in each new ticket, checks the firm's knowledge base and the manufacturer's current manual, and leaves a drafted reply as an internal note on the ticket. It never sends anything to the client: a technician reads it, fixes what needs fixing and sends it themselves. With nobody attached to the run, it draws only on material the whole firm can already see.
InsideAI has kept its own short notes on where things live and how recurring jobs run, so the twentieth question about a client takes two lookups instead of twenty. The prompts that work are saved in a folder per client and shared with the team. When an answer misses, someone flags it in a click and a person on our side reads what went wrong and tightens things so that class of mistake stops.
The same chat is embedded in the firm's own client-facing portal as a bubble in the corner. People sign in once, it knows the page they are on, and the same permissions decide what each of them can reach. Every conversation saves to that person's own account.
A morning queue already answered from the manufacturer's current wording — not a model's memory of last year's.
A new hire gets the tool on day one, and in every system they sign in to themselves it reaches only what that sign-in already does.
A mistake one person catches becomes a mistake nobody makes again.
A technician pastes a client's error log and firewall notes into a public assistant, gets a plausible fix written from general internet knowledge, and the paste now sits somewhere nobody at the firm controls. Tomorrow a different technician starts from zero on the same problem.
The ticket, that client's history and the manufacturer's manual are already there, the answer is limited to what that technician may see, and the reply that worked goes into the shared library for whoever meets it next.
The queue gets smaller every month, because every answer makes the next one easier.
Client documents arrive in every format, spreadsheets get reconciled by hand, and the same rule gets looked up again every time a client asks about it. The practice cannot paste a client's ledger into a public tool and still honour the engagement letter. InsideAI reads the files as data, answers from the current text of the rules, and keeps each engagement's conversations in a shared project with only the people working it.
A dozen partners and staff. Several hundred clients. A very long spring.
The intake folder gets a standing instruction: pull out every invoice total, and flag anything that looks like it holds sensitive personal information. Every file uploaded there — or into any folder inside it — is checked against that instruction as soon as it has been read in, so the pile sorts itself before anybody opens it.
Three years of a client's trial balances go in. InsideAI notices they are the same report from different periods, so comparing them is one question. Ask for the variance by account and you get the real figure worked out across the whole file, with a chart and a table in the answer — and the working visible, so a partner can check it before it goes anywhere.
The text of the regulations the practice works under sits beside its own files, refreshed on a schedule we keep, so an answer comes from the current wording rather than what a model half-remembers. Where a source is kept behind a sign-in, we say so and keep it current by hand. Research now starts from the source and the practice's own past memos at the same time.
Each person schedules their own standing job in plain language — “every weekday at 7am, read my new email and list what each client is still waiting on”. It runs with exactly that person's access and nobody else's, and the summary is waiting in their chat when they sit down. They can change it, pause it or run it on the spot.
Intake that sorts itself, and each engagement's working kept among the people on it.
Research that starts from the rule and from what the practice already decided about it.
The method of the best senior becomes the default for the newest junior.
Paste a client's ledger into a public assistant and it estimates from whatever fits in the window, quotes a rule the way it remembers it, and has no idea this is the client who asked the same question last year. The ledger is now somewhere the engagement letter never mentioned.
The whole file is worked as data, the rule comes from the current text, last year's answer is in the shared project — and nothing the practice puts in is used to train anybody's public model.
The second busy season on InsideAI is easier than the first, because the first one taught it the practice.
Customer service answers specification questions out of spec sheets and a thirty-year engineer's memory. Customers send photos of worn parts, sales asks the same stock questions every day, and the order system is a tool the company built for itself that no software vendor will ever integrate with. InsideAI puts the catalogue, the manuals and that in-house system behind one conversation.
Two plants. Thousands of part numbers. A customer service team of eight.
The catalogue and the engineering documents are indexed. An exact part number or serial counts for more than anything merely close, while everything else is matched on meaning as well as wording, so asking which bracket fits the older housing can still turn up the retrofit note that never uses the word “bracket”.
A customer emails a photo of a cracked fitting. The service rep attaches it to the question and gets the likely part, the replacement procedure from the maintenance manual, and a table of compatible alternatives — with the reasoning on screen, so they can judge it before a customer relies on it.
The in-house order tool is a project we take on rather than a feature request that goes nowhere. It arrives as a list of individual abilities and the company ticks the ones the AI may use: look up an order, yes; change one, left unticked — and an unticked ability is never offered at all, so there is nothing to be talked into.
Meeting notes are connected, design discussions live in shared projects, and InsideAI keeps its own notes on where each kind of answer is found. The person who was not in the room two years ago can find out why a tolerance was changed — and who asked for it.
A customer's photo turns into the right part and the official procedure, not a guess.
Sales can check an order's status without anyone handing the AI the power to change one.
Decisions stay findable long after the people who made them have moved on.
A public assistant has never seen the catalogue, will never connect to an order system the company built for itself, and will confidently produce a part number that looks exactly right and isn't.
The catalogue exactly as written, exact identifiers ranked first, and the in-house system connected because the company asked for it — with only the abilities it chose.
Customer service answers like the senior engineer, and the senior engineer gets their afternoons back.
Maintenance requests come in at all hours, every lease has its own clauses, owners want reports, and each city has its own housing rules. Property managers answer from memory and filing cabinets. InsideAI reads the leases, keeps the local rules current, drafts the overnight maintenance replies once their ticketing is connected and keeps the work on each owner's portfolio in a shared project with the people who manage it.
Fifteen hundred units. Three cities. A phone that never stops ringing.
Leases and building files are indexed. A manager narrows the conversation to one property's folder and asks whether the tenant in 4B can sublet — and gets the answer from the lease itself. Answers come from the company's own documents, not from what leases usually say.
The housing rules for each city the company works in sit beside its own files, refreshed on a schedule we keep. A question about notice periods is answered from the current local text alongside the lease clause it touches, so a manager sees both halves of the answer at once.
Their maintenance ticketing is connected as a project we take on, and from then on overnight requests are read against the building's own documents and the equipment manuals, with a drafted reply left on each request for a coordinator to review and send. Nothing goes out on its own — the draft is what the job does, and a person decides the rest.
Monthly owner spreadsheets are recognized as the same report over time, so “compare repair spend by building, this year against last” comes back as a chart and a table worked out across every row. The conversation about each owner lives in a shared project that only that owner's managers are invited to, so the preparation stays with the people who should see it.
The lease and the law, side by side, for exactly the building in question.
The overnight maintenance backlog is triaged and drafted before anyone clocks in.
An owner meeting prepared in minutes, and kept among the people who manage that owner.
A public assistant can tell you what leases generally say and what housing law generally requires. It has never read your leases, does not know which city the building is in, and cannot see the maintenance request that came in at 2am.
It answers from the actual lease, works from that city's current text, and the 2am request already has a drafted reply against the building's own manuals waiting for a coordinator.
Managers spend their day with tenants and owners instead of in filing cabinets.
Customers leave the app to hunt through documentation, support answers the same how-to questions all day, and engineers get pulled in for anything tricky. Building an AI feature on a model vendor directly would mean building the permissions, the history, the retrieval and the audit trail themselves. InsideAI is all of that already, available in their product, their support queue and their own code.
A growing product. A support team of six. Customers who want answers without leaving the app.
Product documentation and past tickets are indexed together. “Have we seen this error before?” reaches old tickets, past conversations and the docs in one question, and the answer comes from the part that matched rather than the first page of a long article.
The chat drops into the app as a floating bubble in a few lines, using the official Python and JavaScript libraries. Customers are signed in seamlessly, it knows which page they are on — “what am I looking at?” gets an answer about the record on screen — and every conversation saves to their own account.
Their servers use the documented interface to ask questions on behalf of a signed-in user, with that user's permissions, in that user's history. The prompts support perfected become calls the product makes. Each system can have its own key, which shows when it was last used and can be revoked the moment it should stop working.
A newer model became better at their kind of question, and we changed the engine underneath. Their connections, permissions, saved prompts and history belong to the harness, not the model, so their integration kept working and their customers only noticed the answers improving.
A customer asks about the screen they are on and gets the answer only their account should get.
The answer support perfected by hand becomes a feature the product ships.
It keeps getting better without anybody rebuilding anything.
Building straight on a model vendor means building the harness yourself — who is asking, what they may see, where the history lives, how keys are issued and revoked, what gets logged — and rebuilding big pieces of it when you change vendors.
All of that already exists, and the model underneath is a part we swap. They wrote a few lines against the official libraries and got permissions, history, retrieval and a record of every step with it.
Fewer tickets reach a person, and the ones that do arrive with the answer half-written.
Precedent lives in old briefs nobody can find, associates rebuild research a partner already did last year, and incoming document productions land faster than anyone can read them. The firm cannot paste a client's file into a public tool and still keep its duty of confidentiality. InsideAI reads the firm's own work product, answers from the current text of the rules, and keeps each matter's conversations in a shared project with only the people staffed on it.
Forty lawyers. Several practice groups. A billable hour that nobody wants spent searching.
The firm's briefs, memos and templates are indexed. An associate asks in plain words whether the firm has argued this point before and is pointed to the old brief that made it. An exact case or exhibit number counts for more than anything merely close, while the argument itself is matched on meaning as well as wording, so the memo that makes the point in different words can still turn up.
Every matter gets a shared project that only the lawyers and staff on it are invited to, so its conversations stay among the people working it. Each person connects email and documents as themselves, so what InsideAI finds there is exactly what that person could already open — and nothing the firm puts in is used to train anybody's public model.
The folder a document production lands in gets a standing instruction: summarize each document, list the people and dates it mentions, and flag anything that looks privileged. Every file dropped there is checked as soon as it has been read in, so the review team starts from a sorted pile — and a lawyer still makes every call on what is privileged and what is produced.
Each lawyer schedules their own standing job — “every weekday at 7am, read my new email and list every deadline and filing date mentioned”. It runs with that lawyer's access alone, and the list is in their chat when they sit down — a list to check, not a docket: counting and calendaring a deadline stays with the lawyer. When they ask how a deadline is counted, the answer comes from the court's current rule text, refreshed on a schedule we keep; where a source sits behind a sign-in, we say so and keep it current by hand.
Research that starts from what the firm has already argued and the rule as it reads today, not from a blank page.
A matter's work stays among the people on it, and there is a record of who asked what.
A production sorted and a morning's deadlines listed before anyone bills an hour to find them.
An associate pastes a client's contract into a public assistant, gets an answer written from general knowledge that may cite a rule the way it used to read, and the client's confidential document now sits somewhere the engagement letter never contemplated.
The firm's own briefs and the current rule text are already there, the matter's work stays in a project only its team can open, and every step behind the answer is on record, so a lawyer can check its sources before relying on it.
Lawyers spend their hours on judgement, because finding what the firm already knows takes a question instead of an afternoon.
Read them side by side and the pattern shows. The first week is useful. The month after is better, because every connection, every saved prompt and every note makes the others worth more.
A connected system is only as useful as the permissions around it — here they arrive together
Reference material kept current makes every scheduled draft more accurate, without anyone touching the schedule
Every flagged miss is read by a person who can fix it for everyone on the account, not just the person who flagged it
Saved prompts, shared projects and its own notes mean the tenth person to ask gets a better answer than the first
A better model underneath improves all of it at once, and nothing you built has to change
A public assistant only knows what somebody pasted into it — and that paste is the leak
It cannot tell a partner from an intern, so it either knows nothing sensitive or too much
It only works while somebody has the tab open; nothing is waiting when the office opens
The answer that worked dies in one person's history instead of becoming the team's
When it is wrong, nobody who can change it ever reads why
Same frontier models underneath. The difference is everything they are connected to, and everything that stops them reaching further than you allowed.
Straight answer on where we are: InsideAI is early. It is deployed and doing real work inside real businesses, and we are taking on a few at a time on purpose — every deployment gets a person on it, not a ticket queue.
Tell us what your team runs on and what you would want it to answer. We will be straight with you about what fits today and what is still ahead, and coming in now means real say in what gets built next.
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