Skip to main content
Builtwell

Service

AI Pipelines & Agents

LLM workflows, agent orchestration, and retrieval systems engineered for production. Less demo, more deploy.

Timeline

4-10 weeks per pipeline

Pricing

Fixed fee per phase · ongoing engineering retainer

What you get

  • ▸Use-case scoping + cost modeling
  • ▸RAG and retrieval design (vector or hybrid)
  • ▸Agent orchestration with tool use
  • ▸Evaluation harness + regression tests
  • ▸Production deploy with cost + safety guardrails

Who this is for

You’ve seen a demo that worked once and now you need it to work a thousand times. Or your team is gluing prompts into apps and the costs and failures are getting away from you. Or you want an agentic workflow that does real work, not a chatbot that books a meeting.

How we run it

We start by separating signal from theater: what would actually move the business if it ran reliably? Then we design the simplest pipeline that does it: retrieval where retrieval helps, agents where agents earn it, prompts where prompts are enough.

Every pipeline ships with an evaluation harness: a held-out set, a scoring rubric, a regression alarm. If the model provider changes a default, we know in minutes, not weeks.

What you get

  • A deployed pipeline with measurable accuracy and cost ceilings
  • Tool integrations and structured outputs your stack can consume
  • Guardrails: rate limits, content filters, audit logs
  • A handoff so your team can iterate without us

Outcomes our clients see

  • Agentic workflows replacing 1-2 FTE of routine knowledge work
  • AI features that ship and stay shipped (no quiet rollbacks)
  • Cost per task that goes down quarter over quarter

Outcomes

Numbers our clients see.

4-10 wk
Per pipeline, scope to ship
100%
Eval coverage on critical paths
0
Demos that don't ship to production

How we run it

A repeatable engagement.

  1. 01

    Use-case scoping

    We pressure-test the use case before writing a line of code. Most AI work fails because it shouldn't have shipped in the first place.

  2. 02

    Retrieval + agent design

    RAG architecture, agent orchestration, and tool definitions designed for the actual data shape , not a generic template.

  3. 03

    Eval harness

    Regression tests and a real eval set before we tune anything. So you can tell whether a prompt change made things better or just different.

  4. 04

    Production hardening

    Cost monitoring, latency budgets, fallback paths, and observability. The boring infrastructure that makes AI features actually reliable.

Our approach

What we do , and what we don't.

How we run it

  • Eval-driven prompt and model changes
  • Cost + latency budgets enforced from day one
  • Fallbacks for when the model is wrong or down
  • Observability into every retrieval and tool call

What we avoid

  • Demo-grade prompt chains shipped as features
  • Wrapping ChatGPT and calling it a product
  • Frameworks that obscure what the model actually sees
  • Skipping eval because 'it looked good in the notebook'

FAQ

Common questions.

Should we build with OpenAI, Anthropic, or open models?
Depends on the workload, the data sensitivity, and the unit economics. We benchmark on your actual task before recommending , there is no universal answer.
Do you build agents or just RAG?
Both. Agent orchestration matters when the workflow has branching tool use; pure RAG is enough for retrieval-and-answer. We pick the simpler architecture that solves the problem.
How do you measure if it works?
We build an eval set with you in week one , real inputs, expected outputs, edge cases. Every prompt and model change runs through it. No vibes-driven shipping.
What about hallucinations?
Citation-based outputs where possible, eval coverage to catch drift, and explicit fallbacks when confidence is low. We design for the model being wrong, not just for when it's right.

Ready to start a AI Pipelines & Agents engagement?

Schedule a quick clarity call. We'll talk through your goals and where the leverage is, no slide deck, no pitch.

On the call we'll cover:

  1. 01 What you want to achieve and what success looks like
  2. 02 Where the leverage is in your current setup
  3. 03 Whether AI Pipelines & Agents is the right place to start
Chat Now Book a strategy call