Intelligence & Automation
AI Development
We integrate LLMs and classic machine learning into real products: chat assistants, document intelligence, forecasting, and recommendation systems, with the data pipelines and monitoring to keep them honest.
LLM integrations, RAG pipelines, and production ML systems.
- AI features your users actually use
- Accuracy and cost measured, not guessed
- Pipelines that keep models honest
The problem
Everyone says AI could transform your business. Nobody shows you how.
Useful AI work starts with a task whose result can be checked: answering questions from your documents, sorting incoming requests, extracting fields from invoices, forecasting demand. We turn the task into a system with a defined input, an output you can inspect, and a measure of how often it is right.
Language models are one tool among several. For some problems a classical model, a set of rules or a search index is cheaper, faster and easier to explain. We use a language model where it earns its place, and say so where it does not.
What you receive
What a project produces.
Task definition and success measure
A written statement of what the system must do, and how its accuracy will be scored on your own examples.
An evaluation set
A collection of real cases with the correct answers, used to compare approaches and to catch regressions before each release.
The AI feature itself
An assistant, document search, extraction, classification or forecast, built into your product or workflow.
Data pipeline
Ingesting, cleaning and indexing your documents or records, with your access permissions respected.
Guardrails and fallbacks
Rules for what the system may answer, when it must hand over to a person, and how failures are handled.
Monitoring and cost tracking
Quality, speed and spend tracked in production, so drift and price changes are visible.
How we approach it
The positions we take.
Measure before building
We assemble the evaluation set first. Without it there is no way to know whether a change made the system better or worse.
Keep a person in the loop where mistakes are costly
Drafting, suggesting and pre-filling are safer first steps than deciding. Autonomy is added as measured accuracy justifies it.
Design for changing models
Providers update and retire models. The system is built so a model can be swapped and re-tested without a rewrite.
How it runs
Four steps, from the first call to hand-over.
1
Define and measure
We agree the task, collect examples with correct answers, and set the accuracy the feature must reach. You receive a fixed quote for the prototype.
2
Prototype on your data
We compare approaches on the evaluation set and report accuracy, speed and cost per request for each. The choice is made on those numbers.
3
Integrate and harden
The chosen approach is built into your product, with permissions, guardrails, logging and error handling.
4
Launch and monitor
We release to a small group first, watch quality and cost in production, then widen access. Dashboards and alerts are handed over.
Is it a fit
When we are the right people, and when we are not.
A good fit
- You have a repeated task with text, documents or data that people do slowly by hand.
- You can supply examples of good results, or people who can judge them.
- You want to know what it will cost per use before you commit.
Another route is better when
- You want an AI feature because competitors have one, with no task in mind.
- There is no way to check the result, because nobody can say what a right answer is.
- Your data must never reach a third-party model provider and you have no infrastructure to host a model. It is possible, but the scope is very different.
What we ask on the first call
- Which task or decision should improve?
- How is it done today, and how long does it take?
- Can you share examples with the correct answers?
- Where does the data live, and who may see it?
- What error rate is acceptable, and what happens when the system is wrong?
Questions
About AI & Machine Learning.
Something missing? Write to [email protected] and an engineer will answer.
Will you use our data to train someone else's model?
No. We choose providers and settings under which your data is not used for training, and we document what each provider sees before anything is built.
How accurate will it be?
We cannot promise a number before testing on your examples. We measure it in the first phase, and you decide whether to continue based on that result.
Do we need a lot of data?
Not always. Many language model features need clear instructions and a few good examples. Forecasting and classification models need more history.
What will it cost to run?
We measure the cost per request during the prototype and give you a monthly estimate at your expected volume.
Describe what you need.
Describe the problem in plain language. An engineer reads every inquiry and replies within one business day, with a written scope and fixed price before you commit to anything.
- Reply
- Within one business day
- First call
- Free, no commitment
- Confidentiality
- NDA on request, before you share anything
- [email protected]