Capabilities
We are not a platform vendor. Each engagement produces a working system inside your environment — the models, the integrations, the review screens and the evaluation that keeps it honest.
Capabilities · detail
Eight cards, each with the pain it removes and the deliverables you keep at the end. In the demo-heavy world of AI, this is the part that decides whether anything survives contact with production.
People re-type the same decision twenty times a day, and a chatbot that cannot act is no help.
We build task-scoped agents, not chatbots. Each one has a defined remit, a toolbox of approved actions, a confidence threshold and a written record of every step it took.
Invoices, contracts and claims have to be read and keyed by hand — slowly and inconsistently.
Layout-aware extraction with validation rules and reconciliation against your systems. Every field carries a confidence score, and low-confidence fields are queued for a human instead of guessed.
Multi-step processes live in inboxes, so a half-finished job disappears until someone chases it.
Durable workflows with idempotent steps, so a crashed worker never double-posts an invoice. State lives in the workflow engine, not in someone’s inbox.
Two systems that both “work” still need a person to move data between them every morning.
NetSuite, SAP, Dynamics, Salesforce, HubSpot, Jira, Zendesk, Workday, custom SQL and that one system nobody wants to touch. We write the adapter and the tests that keep it honest.
Automation without a review screen turns into blind trust — or into nobody using it.
Automation needs a place where a person can approve, correct and teach it. We build the queue, the diff view and the keyboard-first workflow for the team doing the work.
The system answers confidently from stale or irrelevant sources, and you cannot tell which.
Retrieval that cites its sources and refuses when the answer is not in the corpus. Entity resolution so the same customer is not three rows in two systems.
A single interface change silently breaks the automation the business depends on.
When a UI changes and the robot breaks, the business stops. We move those jobs onto APIs and documented interfaces, and keep a supervised fallback for the systems that have none.
Nobody can prove quality improved — or catch it when a model change made things worse.
A golden set built from your real documents, regression runs on every prompt or model change, and dashboards for accuracy, cost, latency and drift. If a model change would hurt quality, you see it before production does.
How the routing actually works
Every step has a threshold, tuned on your own evaluation set. Above it the action executes; below it the item goes to a person with the evidence attached. That is the whole safety story — not a disclaimer, a number.
routing rule
route(x) = HUMAN if p̂(x) < θ
AGENT otherwise Thresholds are per step, not per system: a wrong supplier name is cheap to fix, a wrong payment is not.
Where we draw the line
Our Responsible AI Policy is not decoration. These are the projects we decline, and the ones we will not put our name on.
Read the Responsible AI Policy →Straight answers
No “contact us for details”. If the answer is “it depends”, we say what it depends on.
Pilot in two to four weeks from kickoff, including discovery and blueprint. That assumes we get read access to a sandbox and a sample of real documents or tickets in the first week — that sample is the usual cause of delay.
Discovery is a fixed fee, the pilot is a fixed price, and production is a monthly retainer with a defined scope. We quote after discovery because the honest number depends on your process, not on a price list. If a process is not worth automating, we say so and charge only for discovery.
No. We integrate with what you run — ERP, CRM, ticketing, data warehouse, and the legacy application with no API. Replacing your core systems is a different project and usually a worse idea than automating around them.
Whatever the task and your constraints justify: frontier APIs where quality matters most, open-weight models you can self-host where data cannot leave your environment, and classical methods where a model is the wrong tool. We benchmark on your data during the pilot and document why we chose what we chose.
That is designed for, not discovered later. Every step has a confidence threshold; below it the item goes to a human with the evidence attached. Nothing with legal, financial or safety consequences is decided autonomously — see our Responsible AI Policy.
Next step
Bring one workflow to a 45-minute call and we will sketch the architecture, name the model choice and give you a realistic pilot scope.
One business day — and the reply comes from an engineer, not a sales sequence