04 — Technology
Applied AI that survives contact with production.
- Typical timeline
- 6 – 16 weeks
- Team
- AI engineer, backend engineer, PM
Retrieval systems, agents, copilots and workflow automation — evaluated, monitored and costed before they reach your customers.
Scoped in discovery and adjusted at each cycle boundary. Nothing here is fixed before we understand the problem.
Retrieval & knowledge systems
Document pipelines, chunking strategy, hybrid search and citation-backed answers over your own corpus.
Agents & copilots
Tool-using assistants scoped to real workflows, with human checkpoints on anything irreversible.
Evaluation & observability
Golden datasets, regression suites, tracing and cost dashboards so quality is measured, not asserted.
Process automation
Document handling, support triage, data extraction and internal workflows wired to the systems you already run.
What you receive
- Use-case assessment & feasibility note
- Working prototype on your data
- Evaluation harness and benchmark set
- Production deployment with guardrails
- Cost and latency monitoring
- Team enablement workshop
Typical stack
Engagement shape
- Typical timeline
- 6 – 16 weeks
- Team
- AI engineer, backend engineer, PM
Indicative only. Final scope and cost are agreed after discovery, and we will tell you if your budget does not match the ambition.
No. We deploy against enterprise endpoints with training disabled, and where policy requires it we run open-weight models inside your own cloud account.
We will say so. A deterministic rules engine is cheaper, faster and easier to defend than a model, and a fair number of the briefs we receive are better served by one.
Often combined with
Tell us what you are building. We will help turn the idea into a digital product people remember — starting with an honest conversation about whether we are the right studio for it.