Engineering service
Cost, evals & production safety
Model routing, budgets, caching strategy, and auditability so AI products survive real traffic, reviews, and finance scrutiny.
- Observability
- Langfuse · traces · costs
- Focus
- Routing · caching · gates
- Typical slice
- 2–6 weeks for baseline
In depth
Shipping AI features without observability is borrowing against next quarter’s incident budget. I put tracing, prompt versioning, and cost visibility in place early — then use evals and routing so improvements are intentional, not accidental.
Parker AI used Langfuse-backed eval harnesses and cost-aware orchestration alongside retrieval strategy; Get Magic emphasised scoped tool access, audit trails, and processor-level safety for tool-enabled assistants.
What you get
Tracing and dashboards for latency, cost, and quality slices
Prompt/version strategy and change management workflow
Model routing and cache-friendly retrieval paths
Tool scope, PII hygiene, and safety checks for high-risk flows
Eval sets tied to releases — not one-off notebooks
How we work
Instrument first
If you cannot see cost and failure modes, you cannot steer.
Define guardrails
Budgets, allowed tools, and human gates where stakes are high.
Tune with evidence
Change prompts, retrieval, or models against evals — not vibes.
Operational playbook
How to roll back prompts, rotate keys, and respond to abuse.
Examples & past work
Outcomes you can expect
- Instrument LLM calls with traces, prompt versions, and cost/latency visibility (e.g. Langfuse).
- Balance token spend and retrieval depth without sacrificing response quality.
- Apply prompt-safety patterns, PII hygiene, and least-privilege tool scopes for enterprise-ready behaviour.
Questions, answered
What is the minimum viable observability stack?
Per-call traces with model, prompt version, token counts, and outcome metadata — plus a handful of golden tests that run on every risky change.
How do you talk to finance or execs about AI spend?
Translate traces into unit economics: cost per successful task, cache hit rates, and which workflows dominate spend — then propose routing and retrieval changes with projected impact.
Do you replace our current LLM vendor?
Only if routing genuinely helps. Often the win is better retrieval, smaller prompts, caching, and safer tool policies — not swapping models weekly.
Book a free 30-minute discovery call to investigate your work and needs
I will map constraints, risks, and a practical first milestone — whether that is agents, retrieval, ingestion, extensions, or full-stack SaaS delivery.
Other services
Multi-agent workflows with planning, memory, typed tool calls, and human checkpoints — from creative-strategy platforms to assistant co-pilots.
Vector + relational retrieval, semantic chunking, and re-ranking so LLM outputs stay grounded when catalogues and documents get large.
Schedulers, retries, and normalised pipelines from ads APIs, social platforms, and internal services — built for scale and observability.