AI Solutions
LLM agents, retrieval pipelines, and ML integrations that unlock real business leverage — not demos.
Most 'AI features' shipped in 2025 were thin GPT wrappers that broke under real users. Our AI work runs in production at scale: properly evaluated, observable, fall-back-ready, and tied to a measurable business outcome. We've shipped retrieval pipelines, multi-agent orchestrators, and full conversational products that actually move revenue.
Built for teams that need senior engineering — fast.
- Founders adding AI features to an existing SaaS
- Operators automating document workflows, support, or sales outreach
- Companies building agentic products on LangGraph, LlamaIndex, or custom orchestration
- Teams who tried 'just plug in OpenAI' and it broke in production
Concrete deliverables. No vague promises.
Production RAG pipelines
Retrieval-augmented generation with proper chunking, hybrid retrieval (keyword + vector), reranking, evaluation, and freshness handling.
LLM agents that don't melt down
LangGraph / custom orchestration, tool calling, retries, guardrails, deterministic fallbacks for when the model has a bad day.
Fine-tuning where it pays off
Honest assessment of whether you need fine-tuning, distillation, or just better prompting. We don't fine-tune for sport.
Eval & observability
Eval suites with golden test sets, regression detection, LangSmith / Helicone tracing, cost-per-conversation dashboards.
Multi-model strategy
OpenAI, Anthropic, open-weights via Together / Bedrock — picked per task, with seamless model swap if pricing or quality shifts.
Safety & compliance
Prompt injection defences, output filtering, PII redaction, data residency awareness for EU customers.
A repeatable, transparent process.
- FramePick the AI use case with the clearest ROI — and the eval metric.
- PrototypeSmallest possible end-to-end pipeline, real data, real users.
- ProductioniseObservability, eval, fallbacks, cost ceilings.
- ScaleCaching, model routing, rate limiting, customer rollout.
- ImproveContinuous eval, retraining, prompt versioning, model upgrades.
Battle-tested tools we typically reach for.
Common questions about ai solutions.
Should I fine-tune a model or just use GPT-4 / Claude?+
Almost always start with a strong prompt + retrieval. Fine-tuning pays off only when you have repeatable structured tasks at scale, or strict latency/cost ceilings. We'll model both during Discovery and recommend honestly.
How do you evaluate AI quality?+
Golden eval sets, automated regression checks on every PR, human eval scoring for subjective tasks, and production sampling. We treat AI quality the same way we treat tests.
What about prompt injection and safety?+
Layered defences: input sanitisation, output filtering, untrusted-content boundaries, PII redaction, audit logs, and red-team test suites.
Can you keep our data out of model training?+
Yes. We use enterprise tiers (OpenAI / Anthropic) that contractually exclude your data from training, or self-host open-weights for the most sensitive workloads.
Ready to start?
30-minute intro call. Concrete plan and fixed pricing in writing within a week.