Retrieval quality
Chunking strategies, metadata filters, and hybrid search so answers cite the right documents—not nearby noise.
AI
AI & Automation
Senior-led execution. Clear milestones. Production discipline.
Useful copilots and automations grounded in your data—with evaluation, guardrails, and human-in-the-loop where stakes are high.
Eval-first
Before wider rollout
RAG
Grounded answers
HITL
High-risk approvals
Outcomes you can measure
What “done” looks like
- Measurable time saved on repetitive workflows
- Safer defaults for PII and role visibility
- Evaluation sets so regressions are caught before users
- Cost visibility on token spend and infra
Typical stack
OpenAI / hosted LLMsVector searchPython workersEval harnesses
Pragmatic AI
Retrieval, summarization, classification, and drafting flows wired to your permissions model. We benchmark prompts/models on your evaluation set and log quality signals—not vanity demos.
Automation that holds up
Workflow engines, scheduled jobs, and event-driven pipelines that replace fragile spreadsheets—business rules stay explicit in code or config, not buried only inside prompts.
Governance and safety
PII redaction, regional data residency choices, retention windows, and escalation paths for low-confidence outputs. Humans approve high-risk actions; models suggest, they do not silently execute.
Capabilities
How we go deeper
Tool use discipline
Strict JSON schemas for tool calls, timeouts, and circuit breakers when downstream APIs wobble.
Operational metrics
Latency histograms, refusal rates, user thumbs, and cost per successful task—so product can steer investment.
Deliverables
Tangible artifacts at every phase
Success metrics, data scope, and fallback UX.
WorkshopsWritten spec
Implementation
RAG / tools / agents as appropriate—not hype-driven.
Governance
Logging, redaction, and review hooks for sensitive paths.
Eval harness
Regression suite for prompts, tools, and model version bumps.
Delivery rhythm
From first call to steady ship
01
Align
Stakeholder workshops, success metrics, and constraint map so engineering decisions trace back to business intent.
02
Blueprint
Architecture sketch, integration list, milestone plan, and explicit risks—signed off before high-velocity build.
03
Build & prove
Sprint demos, code review, automated tests, and staging gates. You see working software every week, not slides.
04
Ship & evolve
Production cutover, observability, runbooks, and a sane handover—plus a backlog-ready rhythm for v1.1 and beyond.
Ideal when you are…
- Support teams drowning in repetitive tickets
- Internal copilots over knowledge bases and runbooks
- Sales ops needing draft-first, human-approved outreach
- Ops teams automating approvals with audit trails
FAQ
Straight answers
Do you fine-tune models?
Only when retrieval and prompt engineering plateau on your metrics. We document data rights and avoid fine-tune paths that compromise privacy commitments.
How do you prevent hallucinations in customer-facing flows?
Grounding, citation requirements, confidence thresholds, and human review queues. We refuse to ship “magic” flows for regulated actions without safeguards.
Can we self-host embeddings?
Yes—where latency or compliance demands it. We compare cloud vs self-host TCO with you before committing.
What is the smallest valuable slice?
Often a single high-volume workflow (e.g. ticket summarisation) with clear before/after timing. We scope thin vertical slices rather than open-ended “AI strategy.”
At a glance
Practical AI features, copilots, and workflow automation that improve speed and decision quality.
Next step
Tell us what you are building next.
We will suggest a discovery slice, rough timeline, and the smallest team that can own outcomes end-to-end.