Services
You can start at any step. Most teams start at one and stop when the thing works.
Before anything gets built.
We shadow the workflow, quantify where the hours go, and write a short document: what to build, what it must beat, and what it will cost to run. Some engagements end here, and that's a fine outcome.
Models, agents, and the plumbing around them.
Retrieval systems, extraction pipelines, classifiers, forecasting, LLM agents with tool access. Written as normal software — typed, tested, reviewable — not a notebook someone is afraid to touch.
Most AI problems are data problems.
Ingestion, cleaning, deduplication, schema design, and pipelines that fail loudly instead of quietly. Where the data isn't good enough yet, we say so early rather than papering over it.
Knowing when it's wrong.
Test sets built from your real edge cases, regression suites that run on every change, audit trails, and human review lanes for the decisions that warrant one.
Into your stack, not beside it.
APIs, queues, internal tools, and CI/CD in the cloud you already pay for. We work inside your conventions and leave infrastructure your team can read.
For as long as you want us.
Cost and latency monitoring, model upgrades as the landscape shifts, and periodic reviews against the metrics we agreed in scoping. Retainer, not lock-in.
COMMON STARTING POINTS
The same underlying tools can solve very different problems. We define the job, the people affected, and the acceptable margin for error before choosing an approach.
Make internal knowledge easier to find without flattening permissions or presenting guesses as facts.
Turn incoming documents into structured, reviewable work that can move through the rest of your business.
Give operators a clearer view of priority, risk, or likely outcomes while keeping the final decision visible and accountable.
Add useful intelligence to a customer or staff product without making the entire experience depend on a chat window.
WHAT PRODUCTION-READY MEANS
Works with your data, permissions, and existing systems through clear interfaces.
Shows what happened, what it cost, and where a person needs to step in.
Runs against known examples so changes can be measured rather than judged by feel.
Handles missing data, provider failures, and uncertain outputs without failing silently.
ENGAGEMENT MODELS
Every model begins with a written scope and ends with something you own. None of them require a long-term commitment beyond the cycle you are currently in.
Step one
A fixed-scope review of one workflow: what it costs today, whether AI helps, and what a first version should do. You keep the document whatever you decide next.
Step two
A dedicated team taking one workflow from prototype to a system your people use daily, in two-week cycles with a working demo at the end of each.
Ongoing
A monthly retainer covering monitoring, cost tuning, model upgrades, and a scheduled review against the measures agreed in scoping. Cancel with a month's notice.
SECURITY & RESPONSIBILITY
Most of our work touches internal documents, customer records, or decisions with consequences. These practices are part of the build, not an add-on.
We ask for the narrowest data access that lets the work proceed, scoped to named systems and removed at handover.
Where the work can run inside your cloud account and your network boundary, it does. Exceptions are agreed in writing before any data moves.
Model providers are chosen with data-retention and no-training terms reviewed in advance, and the interface is written so a provider can be swapped later.
A retrieval system must never show someone a document they could not open directly. Access rules are enforced at query time, not filtered afterwards.
Automated outputs are logged with their inputs, sources, and model version, so a decision can be reconstructed months later.
Every system has a defined confidence threshold and a route to a person. Uncertainty is shown rather than smoothed over.
GLOSSARY
Finding the right passages from your own documents and giving them to a model as context, so the answer cites real sources instead of recalling them from training.
A fixed collection of real examples with known good answers, used to measure whether a change improved the system or merely felt better.
A check around the model — schema validation, allowed-value lists, confidence thresholds — that catches bad output before it reaches a user or another system.
A model given a defined set of tools it can call, with strict limits on what those tools may do and a log of every call it makes.
Adapting an existing model to your data or format. Useful for consistency and cost, rarely a fix for missing knowledge.
A deliberate review step for cases the system flags as uncertain or high-consequence, with the reviewer's decision fed back into evaluation.
Send us a paragraph about the problem. We'll reply with where we'd start.
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