AI workflows. Language models inside your existing processes.
We add AI where interpretation helps, such as classifying requests, extracting data, drafting replies and summarizing cases, inside the systems and approvals you already have.
For teams with a lot of unstructured input: emails, documents, tickets, messages and forms.
AI workflows & integration
We find the steps where a model genuinely helps, connect it to your data with the right permissions, and design the checks around it: validation rules, confidence thresholds, human review and logging. Prompts and evaluations are versioned, so quality can be measured and improved.
- (01)Classification, extraction and drafting
- (02)Grounded in your approved sources
- (03)Human review where it matters
- (04)Evaluations you can rerun
Tools we use for this.
- OpenAI
- Anthropic Claude
- Google Gemini
- Open-weight models
- Retrieval (RAG)
- LangGraph
We choose the stack that fits your requirements. Tool names describe our skills, not partnerships.
Sprint.
4 weeks.
Four weeks on one goal, with working software at the end.
Included
- (01)Use-case and data review
- (02)Model selection and prompt design
- (03)Integration with your systems
- (04)Validation, review and logging
- (05)Evaluation set and quality report
- (06)Documentation and handover
Not included
- (01)Model usage fees (billed at cost or to your own account)
- (02)Training custom foundation models
- (03)Unreviewed automated decisions about people
How it runs
- (01) · Week 1
Pick the step
Choose where AI adds value and how quality will be judged.
- (02) · Weeks 2–3
Build and evaluate
Integrate, test against real examples and tune.
- (03) · Week 4
Roll out
Start with review of every output, then relax where the evidence allows.
Also available as: Workshop (½–2 days) · Build & Support (Staged, ongoing). Compare the formats.
You might also
look at these.
Which model do you use?
The one that fits the task, cost and data rules, often a mix. We can use hosted models from OpenAI, Anthropic or Google, or open-weight models in your own environment.
How do you stop the AI making things up?
By grounding it in approved sources, validating outputs against rules, routing uncertain cases to people and measuring quality on a fixed test set.
Will our data be used to train models?
We use business API terms and providers’ no-training settings where available, or private models when data must not leave your environment. The arrangement is set out in the agreement.