Service
Applied GenAI Integration
Language and multimodal models integrated into real workflows — with structured outputs, provenance, evaluation and review controls.
Most GenAI projects stall between a promising demo and a system people can depend on. The model call is rarely the hard part. The hard part is everything around it: the data it sees, the shape of what it returns, how you know it is right, and what happens when it is not.
We integrate language and multimodal models into existing products and new workflows using structured outputs, model and tool boundaries, confidence handling, provenance, evaluation and review controls — so AI output becomes a reviewed suggestion before it becomes an authoritative record.
What we build
Structured extraction
Schema-first outputs with type and semantic validation, explicit handling of missing values, refusals and truncated responses.
Source traceability
Every extracted value linked back to the page, passage or record it came from, so reviewers can verify rather than trust.
Human-in-the-loop review
Review queues, correction flows and approval gates that preserve human decisions and never silently overwrite confirmed data.
Evaluation & monitoring
Baseline datasets, precision/recall-style checks, model and prompt versioning, latency and cost tracking.
What you can expect
- AI features that fail visibly and recoverably instead of silently
- A model-agnostic integration layer — providers can change without a rewrite
- Measurable quality, cost and latency for every AI step
Frequently asked questions
Which LLM providers do you work with?
We are not tied to a single provider. We choose models based on the task, data sensitivity, latency, cost and your existing environment, and keep a boundary in the code so the model can be swapped later.
How do you stop an LLM from producing wrong data?
You cannot make a model infallible, so we design around that: schema validation, semantic checks, confidence handling, source evidence and a human review step before model output becomes an authoritative record.
Can you add AI to our existing product?
Yes. Most of our GenAI work is integration into an existing workflow. We start by identifying where automation genuinely helps and where a person should stay in control.
Start with a focused discovery workshop
We map the business outcome, the current process, the data and the constraints — then agree the smallest production-ready version worth building.