The demo worked. Then it met production.
Most AI pilots never ship. We find out whether yours can, and hand you the evidence either way.
Pilots that reach production
A minority
Most cited blocker
Evaluation gaps
Why they stall
No ship criteria
Company Data
Docs, tickets, and internal notes
Retrieval
Rank the right source context
AI Agent
Run prompts, tools, and logic
Verified Answer
Return cited answers and handoffs
The model is rarely the problem.
Forrester traced why agent pilots fail to reach production. Three causes account for all of it, and not one of them is the model.
41%
Nobody wrote down what “good enough to ship” actually means. The review has nothing to approve against, so the pilot iterates instead of shipping.
33%
It works on the sample and cannot reach the real thing: the permissions, the live records, the API nobody will open.
26%
What was measured at the start stopped matching what the system does now. The numbers still look healthy while the behaviour has moved underneath them.
We start where most builds finish.
The eval harness comes first. Before we touch the pipeline we build the thing that can tell whether it works, because otherwise every later decision is a guess.
Reliable RAG Systems
Turn company knowledge into AI assistants that answer from the right sources, cite their work, and fail safely.
AI Workflow Agents
Automate repetitive operational workflows with agents that can read context, follow rules, and hand work back to teams cleanly.
Built with the same discipline.
Three systems we run ourselves, on the same eval-first method. Available if you would rather buy the product than the build.
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Answers grounded in your own documents.
Retrieval-based answers from your approved documents only, every response cited, refuses instead of guessing when it doesn't know — start free, no card required.
Explore AnchorWhat changes after the demo.
Reliable systems need grounded context, visible behavior, and clear rules for when to automate and when to escalate.
Ground the system
Good outputs start with good context. We design retrieval, memory, tool use, and decision boundaries before prompt tuning.
Measure it in the open
Every system needs evals, traces, and failure modes you can inspect. We build those in from day one.
Ship where the work happens
The best agent is the one that fits the workflow. We integrate with docs, support queues, GitHub, and internal ops systems.
A RAG assistant should show its sources.
The answer is useful because the user can inspect the evidence behind it.
Generated answer
Enterprise support accounts now require a named escalation owner, a two-hour P1 response target, and a written weekend handoff.
Cited source
Policy PDF p.4
Cited source
Runbook Appendix p.2
Source excerpts
Starting July 1, all enterprise support accounts must assign a named escalation owner. Weekend coverage must include a written handoff. P1 incidents require an initial response within two hours.
GitHub PR review agent
Reads diffs, checks repo rules, and drafts useful review comments.
Incident analysis agent
Clusters error signals and proposes the likely first fix path.
Notes from the work.
What we learn building retrieval, agents, and evaluation — written up while it is still fresh.
Guides
Caching Strategies for LLM Applications: A Practical Guide
Learn exact-match, semantic, and prompt prefix caching strategies to reduce LLM costs and latency in production applications.
9 min read
Comparisons
Open Source vs API Models for Enterprise
Compare open source and API models for enterprise AI: control vs speed, cost predictability vs operational overhead, and compliance trade-offs.
8 min read
Guides
Signs You Need a Dedicated AI Engineer
Recognize when prototypes stall, technical debt compounds, and stakeholder confidence drops—clear signals your AI work needs dedicated engineering ownership.
10 min read
Guides
How to Evaluate Agent Tool Use in Production
Measure agent tool selection accuracy, parameter correctness, and workflow success. A practical framework for evaluating agent tool use in production.
10 min read
Guides
Why AI Pilots Fail in Production
AI pilots fail in production due to curated test data, ignored scale constraints, missing error handling, and skipped operational integration.
8 min read
Comparisons
Retrieval Quality vs Model Quality: Which Matters More?
Retrieval quality determines what context your model sees. Model quality governs how it uses that context. Learn which bottleneck to fix first in RAG systems.
9 min read
What it looks like on the other side.
TruFeed is a user feedback platform, and a WeaveAI customer.
We tried to build this part ourselves first. It worked in the demo and fell over on real data. Now we use WeaveAI's tools and I have stopped thinking about it.
Start Here
If the system has to work after the demo, we should talk.
We scope the workflow, audit the data path, and design the reliability layer before automation starts touching real work.