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.
Product · WeaveAI Cite
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Explore CiteProduct · WeaveAI Ready
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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
What Is an AI Support Deflection Agent?
An AI support deflection agent resolves customer inquiries autonomously before they reach human support. Learn how these agents work and what to evaluate.
11 min read
Guides
Guardrails for Autonomous AI Agents: A Practical Guide
Learn how to implement guardrails for autonomous AI agents: pre-execution validation, output checks, budget limits, and circuit breakers that prevent costly errors.
9 min read
Guides
SOC 2 Compliance for AI Features: What You Need to Know
SOC 2 compliance for AI features requires documented controls over data handling, model operations, and third-party integrations — with auditable evidence.
14 min read
Guides
RAG Evaluation Metrics: What to Measure and Why
RAG evaluation metrics measure retrieval accuracy, generation quality, and end-to-end correctness. Learn which metrics matter for production systems.
10 min read
Guides
Multi Agent Orchestration in Production: A Complete Guide
Multi agent orchestration in production coordinates autonomous AI agents to handle complex workflows reliably at scale with proper error handling and observability.
9 min read
RAG
What RAG Actually Costs Per Query: Real Numbers From a Production System
Inference is not what makes retrieval-augmented generation expensive. A worked cost breakdown from a running RAG system — per query, per ingest, and the fixed line item that dwarfs both.
8 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.