LLM Application Development
A demo is easy. Production is the work.
We build LLM features with the parts that make them maintainable: an evaluation suite, retrieval you can debug, cost controls and a rollback path.
- Eval-driven development
- Traced end to end
- Model-portable
What it is
Evaluation is the unit test of an LLM feature
Without an evaluation suite, every prompt change is a guess and every model upgrade is a gamble. Teams end up frozen — unable to improve the feature because they cannot tell whether a change made it better.
We build the eval set first, from real examples and real failure cases, and treat it exactly like a test suite: it runs in CI, it gates the deploy, and it is the reason you can swap a model on a Tuesday.
- Eval suite before prompt tuning
- Retrieval quality measured, not assumed
- Full request tracing and cost attribution
- Provider-portable architecture
Model families we work with
- Claude
- GPT-class models
- Llama
- Mistral
- Local embedding models
Of features ship with an eval suite
No exceptions, including internal tools
Median inference cost reduction
From routing and caching after baseline
Capabilities
What we build
Retrieval systems
Chunking, embedding, hybrid search and re-ranking tuned against a measured retrieval quality score rather than intuition.
- Hybrid search
- Re-ranking
- Retrieval evaluation
Agentic workflows
Multi-step systems with explicit tool contracts, bounded autonomy and a human checkpoint where the stakes justify one.
- Tool design
- Bounded autonomy
- Human-in-the-loop
Evaluation harnesses
Golden datasets, LLM-as-judge where appropriate, and regression gates wired into your CI pipeline.
- Golden datasets
- Automated judging
- CI regression gates
Guardrails
Input validation, output constraints, refusal handling and the escalation path for the cases a model should not answer.
- Input/output validation
- Prompt-injection defence
- Escalation design
Cost and latency control
Caching, model routing and streaming, with per-feature cost attribution so unit economics stay visible.
- Prompt caching
- Model routing
- Cost per request
Observability
Every request traced with its retrieved context, tool calls and cost, so debugging is reading rather than guessing.
- Request tracing
- Context capture
- Quality dashboards
Use cases
What this looks like in practice
Deployments we have built or scoped, with the sector they landed in.
Knowledge assistants
Grounded answers over internal documentation with citations users can verify.
Document extraction
Structured data pulled from unstructured filings, with confidence scores and a review queue.
- Financial Services
Support copilots
Draft responses for human agents, measured on edit distance rather than deflection alone.
- Retail & Commerce
Process
How we build
01
Define success
What a good answer looks like, written down as examples before any prompt is written.
- Success criteria
- Initial eval set
02
Establish a baseline
The simplest thing that could work, measured — often a strong baseline is enough and the sophisticated version never earns its cost.
- Baseline scores
- Cost model
03
Improve against the suite
Retrieval, prompting and routing iterated with every change scored against the eval set.
- Tuned pipeline
- Regression gate
04
Harden and ship
Guardrails, tracing, cost controls and a rollback path, then a staged rollout behind a flag.
- Production deployment
- Runbooks
Stack
Technologies we use here
Sectors
Where this lands first
Proof
Related work
Also relevant
Related services
Assurance
How we keep this honest
The commitments that matter when the system is making or shaping decisions.
Model portability
Provider access sits behind an interface, so swapping models is a configuration change with an eval run — not a rewrite.
No silent degradation
Quality is monitored in production, not just at launch, with alerting when scores drift.
FAQ
Questions we are asked
Rarely as a first move. Retrieval and prompting solve most problems more cheaply and are far easier to change. Fine-tuning earns its place for format adherence and narrow classification, and we will tell you when you have reached that point.
Bring us the feature that stalled
Most stalled LLM projects are missing an eval suite, not a better model.