We integrate LLMs and AI APIs into the products and systems you already run: retrieval over your own data, vector search, guardrails, evaluation and monitoring that hold up in production.
Bolting a prompt onto a product is easy; making it accurate, safe and affordable at scale is the work. We handle the whole stack from data to monitoring.
OpenAI, Anthropic, Google and open-model APIs integrated into your web, mobile or backend code with streaming, caching and cost controls.
Learn moreRetrieval-augmented generation over your docs, tickets, products and databases with chunking, embeddings and ranking tuned for your content.
Learn moreInput filtering, output validation, PII redaction, topic limits and structured outputs so the AI stays on task and compliant.
Learn moreGolden test sets, automated scoring, regression checks and production dashboards for quality, latency and cost.
Learn moreBenchmark models for your task, fine-tune where it pays off and route requests to the cheapest model that meets the bar.
Learn moreOpen models and vector stores deployed in your AWS, Google Cloud or on-prem environment for data residency and control.
Learn moreRAG over client contract libraries with clause-level citations, permission-aware retrieval and an evaluation suite run on every release.
Visit notes summarised into structured fields with PII handling, a private model deployment and clinician review before anything is saved.
Hybrid keyword and vector search added to an existing catalogue API, with re-ranking and A/B tested relevance.
We are software engineers first. AI features we ship are tested, versioned, monitored and documented like every other part of your system.
We build a test set from real queries before writing prompts, so every change is measured against it.
Data minimisation, encryption, access controls and audit logs designed for GDPR, HIPAA and SOC 2 environments.
Caching, model routing, prompt compression and batching keep inference costs predictable as usage grows.
We integrate in your language and framework and hand over clean code, infra-as-code and runbooks.
Every AI feature we integrate is grounded in your data, guarded against misuse and measured against an evaluation set that grows with real usage.
We work across the modern AI stack and choose components for quality, cost and your compliance requirements.
A proven process to turn your ideas into powerful digital solutions.
We review your product, data sources and goals, and define the eval set that defines success.
Models, retrieval, guardrails, infra and cost model are designed and documented.
We build the pipeline into your codebase with tests, CI checks and staged rollouts.
Production metrics feed back into the eval set and we tune prompts, retrieval and models.
Answers to the questions we hear most often. Need more detail? Talk to our team.
Retrieval-augmented generation fetches relevant pieces of your own content and gives them to the model with the question, so answers are grounded in your data rather than general knowledge. You need it whenever answers must reflect your documents, products or records.
Grounding answers in retrieved sources, requiring citations, validating structured outputs, setting refusal rules for unanswerable questions and continuously scoring outputs against an evaluation set.
Yes. We deploy open models, embeddings and vector stores inside your AWS, Google Cloud or on-prem environment when data residency requires it, and use enterprise API endpoints with no-training guarantees otherwise.
By measuring tokens per request, caching repeated queries, routing simple requests to smaller models, trimming prompts and setting budgets with alerts. We report cost per feature monthly.
Yes. We integrate into your repo and workflow, pair with your team, and leave documentation and tests so they can own the feature after launch.
Tell us what you want your product to do with AI and we’ll propose an architecture, evaluation plan and estimate within a few days.
Or schedule a free consultation
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