Generative AI in production
LLMs, content generation, business copilots. We ship measurable AI use cases — not demos sleeping in a branch.
Everyone did their AI demo in 2023. In 2026, the question is no longer "does it work", it's "does it pay". Cost per inference, accuracy, prompt safety, business guardrails, impact measurement.
We ship scoped, measurable, operable AI use cases: internal copilot, framed content generation, automated classification, product assistance.
What we deliver
Business copilots
Specialised assistants for your domain (legal, support, sales, ops), with access to the right data and guardrails.
Content generation
Product sheets, descriptions, emails, translations generated at scale with an automated quality eval layer.
Eval pipelines
Automated output quality measurement, brand grid scoring, drift alerts.
AI observability
Prompt logs, cost monitoring, output traceability, usage dashboards per use case.
How we work
- 01
Use case audit
We map 5–10 potential cases and score impact × feasibility. Often the right case isn't the obvious one.
- 02
Measured prototype
Prototype scoped to one case, with clear metrics: quality, cost, latency. Quantified go/no-go decision.
- 03
Production deployment
Robust pipeline, guardrails (PII, prompt injection, rate limit), integration with existing tools.
- 04
Iteration & optimisation
Fine-tuning or prompt engineering to lower cost and raise quality, monthly impact measurement.
Use cases
Product description generation
12,000 SKUs described in brand voice, with automated eval and human review only on outliers.
L1 support copilot
Assistant that handles 60% of standard tickets and escalates the rest, with measured customer satisfaction.
Sales call summary
Whisper transcription + LLM summary of key points and next steps, auto-pushed to the CRM.
Stack & tools
- Claude
- GPT-4o / o1
- Llama 3
- LangChain
- LangSmith
- Pinecone / pgvector
- Modal / Replicate