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AI integration
Language and vision models built into your existing software and workflows, with guardrails and measured accuracy.
Calling a model API takes an afternoon; making it reliable takes the rest of the work. We connect models to your data with retrieval, design prompts and tools, add guardrails for privacy and wrong answers, and build an evaluation set from your own examples. The feature ships with accuracy measured against that set, and a person in the loop where mistakes are costly.
What you get
- An AI feature built into your app or workflow, such as extraction, search or drafting
- Retrieval over your documents, tickets or records that respects access permissions
- An evaluation set of real examples and a report of accuracy against it
- Guardrails for personal data, prompt injection and unsupported answers
- Usage, cost and latency monitoring for every model call
- A model choice report comparing hosted and open-weight options for the task
How it works
- 01
Define
We pick one task, collect real examples of inputs and correct outputs, and agree how accuracy will be judged.
- 02
Prototype
Several models and prompt or retrieval designs are tried on the examples and compared on quality, cost and speed.
- 03
Build
The best design is integrated into your software with guardrails, logging, fallbacks and human review where needed.
- 04
Monitor
After launch we track accuracy, cost and user corrections, and re-run the evaluation whenever models or prompts change.
Scope it now.
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Questions
Which AI models do you use?
Whichever performs best on your task within your cost and privacy limits. That may be a hosted model from a major provider through an API, or an open-weight model run in your own cloud. We compare them on your examples rather than on general benchmarks.
What about wrong answers?
We design for them. Answers are grounded in your own sources with citations, uncertain cases are flagged or sent to a person, and the evaluation set catches regressions before a change reaches users.
Will our data be used to train someone else’s model?
Not if it is set up correctly. We choose providers and contract tiers that exclude your data from training, or run models in your own environment, and we document where data is sent and how long it is kept.