You don't need more AI demos. You need AI that works in production.
The pilot impressed the board. Production exposed the rest: hallucinations on real customer questions, costs quietly doubling, agents failing silently, vendor lock-in nobody had modelled. This isn't a prompt problem — it's an engineering problem. Production AI demands the same discipline as any critical system, with one extra layer for non-determinism.
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Your pilot passed the demo. It didn't pass production.
Demo-grade, production-fragile
The pilot held up for thirty minutes in front of a board. In production, the same system hallucinates on out-of-scope questions, blows past the context window on the third turn, and quietly triples the OpenAI invoice with no alert. According to Gartner's GenAI forecast, more than one in three GenAI projects is abandoned before reaching production, and the recurring pattern is the same: a proof-of-concept is not an architecture. An editorial intelligence platform that holds up looks nothing like a Copilot demo.
Zero observability when it breaks
When an agent ships nonsense to a customer, no one can say why. No audit trail, no prompt logs, no model version stamped on the response, no cost ceiling that fired. Your team has a black box at OpenAI or Anthropic and a Slack ticket that says "it stopped working." Without end-to-end traceability, every incident becomes opinion, and the EU AI Act already requires what most pilots never had: a trace, an approver, a human in the loop.
Vendor lock-in and a black box
Your product depends on whatever Claude or GPT decides next quarter — model deprecation, price hike, guardrail change. When a customer asks how the answer was generated, no one can answer. Multi-agent governance and traceable data research aren't luxuries; they're the minimum conditions for an AI system that stays yours, not your vendor's.
From shiny demo to a system that holds
Audit what broke
I sit with your team and walk through the last production incident that derailed. We replay the failing requests, isolate the failure class (hallucination, cost drift, latency, saturated context window, vendor outage), then quantify the incident in lost hours, customers affected, costs nobody capped. The point is to replace "it worked in the demo" with a cold map of the failure modes that actually happen.
Architect for failure
I design the pipeline with degradation paths wired in from the start: fallback chains across providers, deterministic validation before any sensitive output, cost ceilings per request and per day, idempotent retries, circuit breakers. The system is no longer a single Claude call. It's a pipeline that knows what it doesn't know, that verifies before it ships, and that halts cleanly before it costs you real money.
Wire human gates where it matters
Not everything needs a human; nothing critical goes without one. I place review gates on high-impact outputs (customer-facing answers, financial decisions, published content), with a review interface that shows the full context: prompt, response, sources, cost, model, version. The human approves, rejects, or asks for another pass. Everything is logged.
Instrument observability
I wire Langfuse for per-request traces (prompt, response, model, cost, latency), ops dashboards for the KPIs that matter, and alerting on the thresholds you care about: hourly spend, failure rate, escalation rate, quality drift. You know in real time what your AI is doing, what it's costing, and what it just broke. Not three days later through an unhappy customer.
What AI that holds looks like
Five specialised agents running daily, with no human intervention, on the editorial pipeline: research, drafting, fact-checking, voice, translation. None is an LLM wrapper; each has a defined role, an assigned model (Claude Opus for judgement, Sonnet for volume, GPT-5.1 on specific tasks), a budget and escalation rules. Read how they were wired.
More than thirty deterministic validation rules that block bad outputs before they ship: Zod schemas on structured data, length and tone checks, cross-language consistency, unsourced citation detection. An output that leaves the pipeline is an output that passed every gate, not an output you hoped was clean.
Fifteen hundred automated tests covering both the code and the AI behaviour: unit tests on MCP tools, integration tests on pipeline stages, end-to-end tests on real cases. When a prompt changes, you know within the minute whether you just broke a known regression.
5 agents, 24/7, reliable
- 5 specialised AI agents running 24/7 in an 8-step pipeline: not an LLM wrapper, a real production line
- 30+ deterministic quality checks block bad outputs before they ship: schemas, consistency, format, sourced citations
- Full prompt + response + cost audit per article, debuggable and replayable, no black box
Three services, one discipline
AI workflow automation
Multi-agent pipelines engineered for production, not just demos: control plane, audit trails, per-agent budgets, and quality gates at every stage.
Data research systems
Production research systems with source attribution and coverage scoring: every fact traceable to a URL, a date, and a reliability score.
Computer use automation
When the agent needs to drive a real interface, the engineering that keeps it from going off the rails on software with no API.
Built to hold
Common questions
How do you decide where AI fits, and where it doesn't?
Three criteria, in this order: error tolerance, traceability required, cost of a false positive. AI fits when an error has a bounded cost and a human can catch it before impact; it does not fit when a single bad output exposes the company legally with no human gate. We always start with an audit of the targeted workflow: where AI actually removes work, where it adds risk, where a simple tool consolidation would do the same job at a tenth of the cost. Not every function deserves an agent.
What if Anthropic deprecates Claude, or OpenAI changes its prices?
That's the scenario we architect for from the start. The pipeline is vendor-agnostic via an abstraction layer: every agent's model assignment changes by configuration, not by rewrite. In practice, we keep at least two providers wired in a fallback chain (Claude primary, GPT in fallback, deterministic rules as last resort). When a vendor changes prices or deprecates a model, you flip the assignment and rerun the regressions. The same multi-agent governance that makes the system reliable makes the migration trivial.
How do you handle hallucinations on customer-facing surfaces?
Three layers, in priority order. Layer one: deterministic validation before every output (Zod schemas on structured data, citation-source verification, format checks). Layer two: retrieval-augmented generation against a resolved-entity knowledge base, never a generic web search. Layer three: human gate on high-impact outputs, with a review dashboard that shows prompt, response and sources. Hallucinations don't go away; they get intercepted before they reach the customer. On regulated surfaces, nothing ships without a human signoff, and that's an architecture choice, not a workaround.
Maintenance: is this a one-time build or a continuous engagement?
Both models exist, but the reality of production AI leans toward continuous engagement. Models change (Claude 4.7, GPT-5.1, Gemini 3), prices move, vendor guardrails shift; an AI system wired in March 2026 does not behave like the same system in March 2027. In practice: a three-to-five-month initial build, then a light monthly retainer for regressions, observability and model arbitration. You keep the code, the infrastructure and the prompts; I stay available for structural evolutions. The same approach applies to custom business tools that depend on an external model.
How does this fit with EU AI Act audit requirements?
The AI Act applies from August 2026 on high-risk systems, and the patterns it requires are precisely the ones that make AI reliable in production: complete audit trails (Article 12), wired human oversight (Article 14), risk management documentation (Article 9). If your AI system touches HR, financial, legal or health decisions, you are in scope. The right reflex is not to wait until August 2026 and rebuild everything; it's to build with these patterns now, because they pay their cost in reliability before they ever pay it in compliance. For the regulatory side in depth, see EU AI Act compliance.
Stop paying for demos. Build AI that runs Monday morning, every Monday.
Bring your last AI pilot. We'll replay the requests that broke, map the failure modes, and design the minimum redesign — the one that gets it holding in production.
30-min call. No commitment. Reply within 24h.