AI that works
in production.
Practical AI inside your existing software scoped honestly, measured rigorously and designed to stay reliable when the model is wrong.
- Approach
- RAG · fine-tune if needed
- Models
- Anthropic · OpenAI · OSS
- Prototype by
- week 2
- Includes
- eval framework
Practical AI. Measured, scoped and honest about limits.
Most AI projects fail because someone built a demo and called it a product. The demo impressed a board; the production system hallucinated into a support ticket or an invoice and got quietly switched off six months later.
We start with the question no one asks first: what happens when the model is wrong? Then we build the AI integration with that answer already designed in confidence scoring, human review paths and an evaluation framework that measures accuracy continuously, not just at launch.
From scoping to feedback loops.
Scoping & evaluation
We start with an honest assessment of where AI actually helps and where it adds complexity without adding value.
Internal copilots
AI assistants that know your data, your terminology and your processes not a general chatbot pointed at your docs.
Document understanding
Extract structured data from invoices, contracts and forms. Route, classify and summarise at scale.
Semantic search
Search that understands meaning, not just keywords. Across your knowledge base, your product catalogue or your codebase.
AI-powered automation
Combine LLMs with your existing workflows to handle edge cases that rule-based automation cannot.
Model integration
API-level integration with OpenAI, Anthropic and open-weight models. We pick the right model for cost, latency and quality.
Safety & guardrails
Output validation, hallucination mitigation and human-in-the-loop checkpoints where the stakes are high.
Feedback loops
Capture where the model was wrong, feed corrections back and improve systematically over time.
Problem framing first. Prototype before committing to architecture.
- Week 001
Problem framing
We define what the AI needs to do, what good output looks like and what happens when it is wrong. Most AI projects fail because nobody did this first.
- Week 1–202
Prototype
A working prototype against your real data. We test the model limits before committing to an architecture.
- Week 3+03
Integration build
The AI layer integrated into your existing software not a separate tool your team has to remember to use.
- QA04
Evaluation
We define an evaluation set, measure accuracy and have your domain experts review edge cases before going live.
- Launch05
Monitored rollout
Gradual release with confidence scoring visible to operators. Human review paths for low-confidence outputs.
- After06
Continuous improvement
Feedback loops, model updates and quarterly reviews to keep quality high as your data and use cases evolve.
A recent build contract review copilot for a legal services firm.
Cap set at 1× annual fee below firm standard of 2×. Review advised.
Specified as New York conflicts with standard jurisdiction clause.
No mutual opt-out window. Binding auto-renewal after 12 months.
Model-agnostic. RAG over fine-tuning for most cases.
We are model-agnostic and pick based on cost, latency and quality for your specific use case. Most production systems we build use retrieval-augmented generation rather than fine-tuning cheaper, updatable and auditable.
Project or retainer. Accuracy benchmarks included.
AI integration project
For a specific, scoped AI capability.
- Fixed price after the problem framing week.
- Prototype in week two, production in six to ten.
- Evaluation framework and accuracy benchmarks included.
AI product retainer
For ongoing AI product development.
- A dedicated AI engineer embedded in your team.
- Monthly cadence: new features, evaluations, model updates.
- Quarterly accuracy reviews and roadmap.
What teams ask us before starting an AI project.
How do you prevent hallucinations?
Do we need to fine-tune a model?
What about data privacy will our documents go to OpenAI?
How do you measure whether the AI is actually working?
Can AI actually replace a human in our process?
Tell us the task you want AI to handle.
We will tell you whether it is a good fit for AI, what accuracy you can realistically expect and how long it will take.