Not a chatbot bolted to your website. Assistants, document pipelines and forecasting models wired into the systems you already run — with permissions, citations and a human in the loop where it matters.
High-volume reading, typing and judgement on repetitive data is where this earns its cost. Everything else is usually a report and a better workflow.
Every engagement starts by agreeing what it should save — hours, error rate or turnaround — so there is something to measure it against.
Scanned bills, POs, delivery notes and forms read into structured records, matched and queued for review.
Ask your own documentation and records in plain language, with answers that point at the source.
Demand, replenishment and bottleneck prediction trained on your movement history rather than a generic model.
Multi-step work that used to need a person watching an inbox — with approvals kept where they belong.
These are decided before the first model call, not retrofitted after an incident.
No training on your content. Where policy requires it, models run in your cloud tenancy or on your own hardware.
The assistant sees exactly what the person asking can see. Retrieval is filtered before the model, not after.
Every response points at the documents or rows behind it, so anyone can check it in one click rather than trust it.
Anything that writes, pays or approves goes through your existing chain and is logged and reversible.
Claude, Gemini, OpenAI or open-weight — chosen per task and swappable, so a price or policy change is not a rebuild.
Accuracy and hours saved tracked against a baseline taken before launch, reviewed every quarter.
A written evaluation set from your own documents and edge cases, scored before launch and re-run on every model or prompt change.
The manual path stays intact behind every automation, so a provider outage or a bad week degrades to how you work today rather than stopping it.
Token and infrastructure spend estimated against real volumes during architecture — the cheapest model that clears your accuracy bar is the one we use.
A named person in your team is trained on the prompts, the eval set and the review queue, so the system does not depend on us being reachable.
We pick per task and keep you portable between providers — nothing here locks you to one vendor's pricing.
Answered the way we'd answer them on the call.
Ask us something elseOften you don't. It earns its cost where there is high-volume reading, typing or judgement on repetitive data. If a report and a better workflow solve it, that is cheaper and we will say so.
No. We use providers under no-training terms, and where policy demands it we run open-weight models inside your own cloud tenancy or hardware instead.
It is designed to. Low-confidence extractions go to a review queue rather than straight through, answers cite their sources so anyone can check, and nothing writes or pays without your approval chain.
No. The model sits behind our own interface, so Claude, Gemini, OpenAI or an open-weight model can be swapped per task when pricing, policy or capability changes.
We estimate monthly token and infrastructure cost during the architecture stage, against your real volumes — and design around it, since the cheapest model that passes your accuracy bar is usually the right one.
Possibly, and that is worth knowing before you spend. If four systems disagree about the same number, the ledger comes first — which is a smaller project than an AI programme built on top of the disagreement.