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04 · Service
AI & Machine Learning LLMs, RAG, and ML — shipped, not demoed.
We turn AI prototypes into production systems with measurable quality, predictable cost, and the evaluation harnesses that keep regressions out.
- Eval score (1–10) at launch
- 8.6
- Cost via caching + routing
- −61%
- Citations on every answer
- 100%
- Models benchmarked per build
- 10+
LLMs · RAG · Evals · GuardrailsCurrently building a research copilot for Northstar Labs
04 · AI & Machine Learning
LLMs, RAG, and ML — shipped, not demoed.
Engagement
8–14 weeks · Discovery → Build
Overview
A demo with a great prompt isn't a product. We build the eval harness, the retrieval stack, the cost controls, and the guardrails that make AI shippable.
Eval harnesses gate every deploy
Cost + latency visible to the whole team
Hybrid retrieval (BM25 + vector) per domain
What we deliver
Exactly what lands in your repo.
No mystery deliverables. Here's the shipping inventory you get at the end of an engagement.
LLM integrations with Claude, GPT, and open models
RAG pipelines with hybrid retrieval and citations
Eval harnesses, A/B frameworks, and guardrails
Custom ML models — training, serving, monitoring
Prompt caching, batching, and cost dashboards
Safety, red-teaming, and PII handling
Stack & tools
The stack we reach for first.
Opinionated, never religious — we'll meet your existing stack if it's a good fit.
AnthropicOpenAIpgvectorLangChainPyTorchModalRagas
How we ship
A predictable four-step rhythm.
Friday demos, weekly to staging, and the senior team you met in sales — start to finish.
- 01
Define the eval
Before a single prompt, we define what 'good' looks like — measurably.
- 02
Build the retrieval
Hybrid retrieval (BM25 + vector) tuned per domain, with provenance.
- 03
Wrap with guardrails
Citation contracts, PII filters, cost budgets, and graceful fallbacks.
- 04
Ship + monitor
Production rollout behind feature flags, with eval dashboards on day one.
Spotlight case
Read the full case studyA research copilot lawyers actually trust.
Northstar Labs
Hallucinations were a non-starter for Northstar's legal team. We built citation-contracted RAG over their case library, with eval-gated rollouts and a graceful 'I don't know' fallback. Adoption hit 78% in eight weeks.
- 0%
- Weekly active adoption
- 0
- Uncited claims shipped
- −0%
- Inference cost / query
How we work
The signals you can hold us to.
We earn trust by making it cheap to verify our work — in your tools, on your calendar, in your repo.
- Eval-gated deploys — no prompt change ships without a score
- Citation contracts: every claim links to a source
- Prompt caching, batching, and model routing for cost
- PII filters, red-teaming, and safety reviews on every build
“Codlinx is the rare team that talks evals before prompts. Our partners stopped second-guessing the tool because every claim links to a source they can read.”
Maya Okafor
Head of AI, Northstar Labs
“We had a demo that wowed the room and broke in production. Codlinx gave us the eval harness that made it actually shippable.”
Jonas Weber
Head of ML, Cortex Labs
“RAG that cites its sources and a cost dashboard from day one. Our legal team signed off without a single redline.”
Amelia Stone
Director of AI, Meridian
FAQ
Common questions about ai & machine learning.
- Which model should we use?
- Whichever wins the eval. We benchmark Claude, GPT, and open-weights against your task and pick on quality + cost.
- How do you handle hallucinations?
- Citation contracts ('every claim links to a source'), eval-gated deploys, and graceful 'I don't know' fallbacks.
- What about cost?
- Prompt caching, model routing, and request batching are baked in. We publish a cost dashboard with every rollout.
Other services
Most engagements run two or three in parallel.
Ready for ai & machine learning?
Tell us about your project.
A scoped plan, a senior team, and a price — inside 24 hours.