We build on established components
Model choice follows the requirement, accuracy, cost, latency, data residency, not the other way around. Everything runs on standard tooling your team or any developer can maintain.
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Most AI projects stall because they live in a chat window, disconnected from where work actually happens. We build systems that read your files, act in your software, and hand a person the final call.
A general model doesn't know your contracts, your pricing rules, or your last three years of tickets. Without your data, the answers are plausible and wrong.
A model that can't read your CRM or write to your ticketing system isn't a system. Someone still copies the output across by hand.
Without sources, logs and a review step, the team quietly stops using it, usually after the first confident mistake.
Pilots are optimised to impress in a meeting. Production needs error handling, monitoring, cost control and someone accountable when it breaks.
Ask a question in plain language, get an answer with the source attached. Built on your contracts, manuals, policies and past work, so the answer can be checked rather than trusted blindly.
Extract fields, classify, summarise and route, across every document, not a sample. Invoices, contracts, applications, tickets, forms. What took an afternoon per batch runs continuously.
Replies drafted in your inbox, requests sorted into the right queue, records filled from unstructured text. Your team reviews and approves rather than starting from a blank page.
Search, recommendations and in-product assistance built on your data, designed to hold up in front of customers, with the latency and cost profile that requires.
Multi-step work, look something up, check it against a rule, update a record, notify a person, running within limits you set, with every action logged.
Draft replies grounded in your help centre and past tickets. Route by intent and urgency. Surface the three most similar resolved cases to whoever picks it up.
Summarise calls into CRM fields, flag risk in open deals, prepare account briefs before a meeting from everything already recorded about that customer.
Read incoming documents, extract what matters, validate it against your rules and post it into the system of record. Exceptions go to a person; the rest goes through.
Search across contracts for clauses, dates and obligations. Compare an incoming document against your standard terms and get a list of what differs.
One place to ask how something works, answered from your own documentation, with links to the source. New hires stop interrupting senior people to find out.
We review the process, the data behind it, and what an acceptable answer looks like. You receive a written scope, a fixed price, and an honest read on whether AI is the right tool, sometimes it isn't.
Before any model work, we get your documents and records into a form that can be searched reliably. Most of the quality difference between AI systems is decided here.
We assemble the system and test it against real examples with known correct answers, so accuracy is measured rather than assumed. You see results each week.
It goes where the work already happens, your CRM, inbox, ticketing system or internal tool. No new tab to remember.
Logging, cost controls, error handling and alerts. You can see what the system did, why, and what it cost.
The system drafts, proposes and prepares. A person confirms before it reaches a customer or changes a record that matters.
Output links back to the document or record it came from, so it can be verified in seconds.
We build an evaluation set from your real cases and report performance against it, before and after launch.
We work within your accounts and infrastructure, and we don't train third-party models on your content. Where the work requires it, we deploy models that keep data inside your environment.
Per-request cost is monitored and capped. You know what the system spends before it surprises you.
Model choice follows the requirement, accuracy, cost, latency, data residency, not the other way around. Everything runs on standard tooling your team or any developer can maintain.
Any language model can. That's why we ground answers in your own documents, attach sources, evaluate against known-correct examples, and put a review step in front of anything consequential. The goal isn't a system that's never wrong, it's a system where being wrong is visible and cheap.
No. Your content isn't used to train third-party models, and we work inside your accounts wherever possible. When data can't leave your environment, we deploy models that run within it.
Two parts: the build, quoted as a fixed price against a written scope, and the ongoing model and infrastructure spend, which is usually modest and always monitored. We estimate running cost during the assessment, before you commit.
Most AI integrations reach production in four to ten weeks, depending on the state of the underlying data. If your documents and records need work first, we'll say so in the assessment rather than discovering it halfway through.
Not necessarily, preparing it is often part of the work. But it's the honest answer to why some projects take longer: the model is rarely the hard part.
In most cases, yes. If a tool has an API, we can read from it and write to it. Where one doesn't, we'll tell you what the workaround costs before you decide.
We keep the system's logic separate from any single provider, so a model can be swapped without a rebuild. That's also why we avoid tying core functionality to features only one vendor offers.