Autonomous agents
Agents that take a goal, use your tools and finish the job, with the guardrails and human checkpoints that make them safe to trust.
Applied-intelligence lab
Intelligence, put to work.
Most AI stops at the demo. We build the other 90%, the agents, pipelines and guardrails that survive production and return a number you can defend.
Intelligence pointed at workflows where the numbers actually matter
AGENTSAUTOMATIONAPPLIED MLRAGEVALSMLOPS
Project estimator
Price your project
in under a minute.
Website, app, product, automation or AI. Answer a few questions and watch the estimate build live. No email required to see the number.
01 What do you want to build?
02 How much scope?
· Expected monthly volume
03 What else do you need?
04 Timeline
· Send this estimate to your inbox
How it works: pick a project type (website, web app, mobile, e-commerce, MVP, AI agent, automation or data), set scope and volume, add what you need. Typical outcomes from real Crebos AI engagements: AI agent builds from $18,000 (5–7 weeks), automation from $14,000 (~4 weeks), MVPs from $28,000 (~10 weeks). Every figure carries a ±15% indicative range and is refined in a free 30-minute scoping call.
What we build
Five ways intelligence
earns its keep.
Workflow automation
The repetitive middle of a process, handled. Fewer handoffs, fewer errors, hours returned.
Applied ML
Models pointed at a real prediction, churn, risk, demand, trained on your data and shipped behind an API.
Retrieval & RAG
Your knowledge, answerable. Grounded responses with citations, so people trust what comes back.
Evals & MLOps
Measurement, safety rails and monitoring. The part that decides whether AI is a toy or an asset.
Strategy & audits
Where does intelligence pay off, and where doesn't it. A straight answer before you spend.
The loop
How a demo becomes
a system.
Clever is easy. Dependable is engineering. We close the gap between a promising prototype and something you can put in front of real users.
Book a session- 01
Frame
We find the one workflow where intelligence returns the most, and define what "working" means in numbers.
- 02
Prototype
A thin end-to-end slice, fast. Real data, real tools, so we learn what breaks before it's expensive.
- 03
Harden
Evals, guardrails, fallbacks and human checkpoints. The unglamorous part that makes AI dependable.
- 04
Run
Monitored in production, improving on evidence. We stay on it, because a model left alone quietly drifts.
Meet your next teammate
Agents that work
while you don't.
This is the shape of what we ship: autonomous, tool-using, monitored. The scene is live 3D, drag it. Our agents are just as alive in your stack.
Price an agent build →Proof
Measured, not promised.
Lives past the demo
The demo is the easy 10%. The agents, pipelines and guardrails that reach production are what we build.
Returned per person / month
Typical hours handed back when a repetitive workflow is automated end to end and monitored.
Ship with evals
If we can't measure it, we don't call it done. Every system goes out with a way to prove it works.
Most AI stops at the demo. We build the part that ships.
FAQ
Straight answers.
What does an AI agent project cost?
Production AI agent builds at Crebos AI start around $18,000 and typically run 5–7 weeks. Pilots (one workflow, proving value) start lower; multi-team platforms run higher. The estimator gives you a live number in under a minute, no email required.
What's the difference between an AI demo and production AI?
A demo shows the happy path. Production AI adds the other 90%: evals that measure quality, guardrails and fallbacks for when the model is wrong, human checkpoints, monitoring for drift, and integration with your real tools and data. That gap is exactly what we build.
What is RAG and when do I need it?
RAG (retrieval-augmented generation) grounds an AI's answers in your own documents and data, with citations, instead of relying on the model's memory. You need it when answers must be current, verifiable and specific to your business, think support knowledge bases, policy lookups, internal search.
Do you maintain systems after launch?
Yes. Go-live is the middle of the job. Every system ships with monitoring and evals, and we stay on it, because a model left alone quietly drifts. Managed support is available as a standard add-on.
Book a session
Bring one workflow. We'll tell you if AI earns its place.
A 30-minute working session, no pitch. You leave with a straight answer on where intelligence pays off and where it doesn't.