01 · AI Product Engineering

AI product engineering that survives real users.

LLM features, autonomous agents, RAG pipelines, and evaluation harnesses — engineered by a senior team that ships production AI, not demos. OpenAI, Anthropic, LangChain, and vector DBs, wired to your product with guardrails, cost control, and observability from day one.

Starts at $499 · Typical range $499–$2,499 · Model-agnostic

60–80%
Avg. AI bill cut
<300ms
P50 latency target
4wk
Prototype → prod
100%
Evals before ship

What you get

Built to ship, not to sit.

Every engagement pairs senior engineering, product design, and AI expertise on a single small team. No account managers, no sub-contracting — the people writing the code are on your calls.

Senior team
Weekly ship
Owned by you
Measured
  • 01Retrieval-augmented generation with proper chunking, hybrid search, and re-ranking.
  • 02Agent workflows with tool use, memory, and human-in-the-loop where it matters.
  • 03Eval harness with golden sets, regression tests, and CI gates — before you ship.
  • 04Cost + latency instrumented per request; caching and prompt compression built in.
  • 05Guardrails, PII redaction, and prompt-injection defenses appropriate to your industry.
  • 06Model-agnostic architecture — swap OpenAI, Anthropic, or open-source without a rewrite.

How it works

From raw prompt to production-grade AI feature.

User intent
Query, context, tools, constraints.
Step 01
Retrieve
Hybrid search + re-rank.
Step 02
Reason
LLM + tools + memory.
Step 03
Guardrail
PII, injection, schema.
Step 04
Evaluate
Golden set + CI gate.
Grounded response
Cited, cached, measured.
Instrumented, versioned, and observable end-to-end.
Weekly demoLoom updatesSlack channel

Outcomes

What changes after we ship.

Outcome 01

AI that ships behind a metric

Every feature ships against a measurable target — task completion, deflection, time saved. If it doesn't move the number, it doesn't ship.

Outcome 02

Predictable AI unit economics

Cost per request tracked from prompt one. We regularly cut inherited AI bills by 60–80% with the right model, cache, and prompt strategy.

Outcome 03

Proprietary AI moat

Your evals, your retrieval index, your fine-tunes — durable artifacts that stay yours, not the model provider's.

Process

A calm, weekly rhythm.

01
Week 1

Opportunity map

Where AI actually earns its keep in your product — and where it's a distraction that burns credits.

02
Week 2

Prototype + eval

Ship the sharpest feature behind an eval harness. Measure real accuracy, not vibes.

03
Week 3

Production hardening

Guardrails, caching, fallbacks, observability. Load-test the real user path.

04
Week 4

Launch + iterate

Flag-gated rollout, telemetry, weekly eval iteration until the target metric moves.

The difference

The agency way vs. the Augere way.

Typical agency
  • Kick-off decks, weekly status theater, quarterly roadmaps.
  • Junior devs behind an account manager wall.
  • AI demo that dies on real user traffic.
  • Handoff = a Notion doc and good luck.
Augere Labs
  • Shipped surface area every Friday. Loom, not slides.
  • The senior engineers writing the code are on your calls.
  • Evals, guardrails, cost + latency instrumented from day one.
  • Runbooks, dashboards, and a warm 30-day support tail.

Stack we ship on

OpenAIAnthropicGeminiLlamaLangChainLlamaIndexpgvectorPineconeWeaviateBraintrustLangfuseVercel AI SDKOpenAIAnthropicGeminiLlamaLangChainLlamaIndexpgvectorPineconeWeaviateBraintrustLangfuseVercel AI SDKOpenAIAnthropicGeminiLlamaLangChainLlamaIndexpgvectorPineconeWeaviateBraintrustLangfuseVercel AI SDK

Deliverables

Everything you walk away with.

You own the repo, the model configs, the evals, the dashboards. No lock-in, no repo held hostage, no black-box handoff.

  • Production LLM / agent / RAG feature deployed into your product
  • Model-agnostic architecture (OpenAI, Anthropic, open-source ready)
  • Retrieval pipeline: chunking, embeddings, hybrid search, re-ranking
  • Eval harness with golden dataset and regression suite
  • Cost + latency dashboards, per-request tracking
  • Guardrails, PII handling, prompt-injection defenses
  • Versioned prompt registry and prompt library
  • Runbook for on-call, incident response, and model updates

They cut our LLM bill by 71% in the first month and shipped the eval harness we should have built a year ago.

Head of Product, Series A SaaS, US

FAQ

Questions, answered.

Which AI models do you use?+

Model-agnostic. We choose GPT-5.x, Claude, Gemini, or open-source (Llama, Mistral, Qwen) per task based on quality, cost, latency, and privacy. Architecture supports hot-swap without a rewrite.

Can you build agents that actually complete tasks?+

When the task is bounded, tools are well-defined, and there's a human-in-the-loop for the last mile — yes. When it isn't, we'll tell you 'workflow' beats 'agent' and save you months.

How do you handle hallucinations?+

Retrieval grounding, structured output schemas, and evals proportional to risk. Financial, medical, and legal contexts get stricter treatment — including citations and refusal patterns.

How do you control AI costs?+

Cost per request is instrumented from day one — model choice, semantic caching, prompt compression, and batching. Weekly cost review during the build.

Do you fine-tune models?+

When it earns its keep — typically for style, structured output, or domain vocabulary. Retrieval + strong prompting solves 80% of cases first.

Ready to build?

30-min intro call. No slides. We diagnose the shortest path from where you are to shipped.

Book a free intro call