Make intelligence work.
Built into your business. Independently validated. Continuously improved.
Trusted by teams at global enterprises and growing companies
The hardest step in AI is the one after the pilot.
Most organizations now use AI somewhere. Far fewer run it in production with results they can measure and defend. Closing that gap takes integration with real systems, independent testing and people who keep it accurate. That is the work we do.
of organizations report enterprise-level EBIT impact from AI, essentially unchanged from the year before.
McKinsey · State of AI, Aug 2026of businesses are scaling AI into production, and only 6% say their data is fully ready to support it.
Dun & Bradstreet · AI Momentum Survey, Jul 2026of CIOs say traceability or explainability gaps have delayed or stopped AI projects from reaching production.
Dataiku / Harris Poll · CIO survey, Feb 2026Figures from the publishers' own releases. For the full evidence base and what to do about it, read From Pilot Purgatory to Production.
Three shifts shaping AI programs in 2026.
The questions clients bring us have changed. Each shift maps to one of our practices.
Agents now act, not just answer.
Agents read and write to business systems through tools and protocols such as MCP. That raises the bar for integration, permissions and a tested path back to a person.
How we deploy →Reviewers expect evidence.
Boards, auditors and customers increasingly ask for documented testing, and EU AI Act obligations continue to phase in. Independent evaluation turns that request into a file you can hand over.
How we assure →Models change every quarter.
New models and price points arrive constantly. Systems need re-evaluation, cost-aware model routing and monitoring of accuracy, not only uptime.
How we operate →Build it, test it, keep it working.
Deploy, Assure and Operate work as a single cycle. What we build, we test independently. What passes, we run and improve. Every engagement has a written scope, an accountable owner and a clear exit, and starts with a thirty-minute working session.
Deploy
Mission Pods pair a senior forward-deployed engineer with a delivery lead inside your team. One workflow, a written definition of done, and a first production release within two weeks.
Assure
Independent evaluation and red-teaming by specialists who are separate from the build team. Domain test sets built with credentialed experts, and findings that are ranked and reproducible.
Operate
Monitoring that measures whether your AI is right, as well as whether it is running: accuracy drift, cost per task, exceptions and tool-use failures, with human escalation built in.
From first call to production, in weeks.
Every step has an owner and a written criterion. Milestones are invoiced only when their evidence is met.
Working session
Thirty minutes on your most important workflow. You leave with a written range and a recommended starting point.
Scope in writing
One workflow, a definition of done, owners on both sides and the evidence that clears each milestone.
First release
A deliberately focused release, live in production, with the agreed evidence attached.
Independent proof
Evaluation against the agreed accuracy bar, run by reviewers with no stake in the launch date.
Operate and improve
Monitoring, quarterly re-testing, and the next workflow scoped from what operation has taught us.
Starting points for common workflows.
Each package combines building, testing and expert review around one workflow. We adapt it to your size, budget and setup.
Talent-to-Bill
AI screening at volume with human approval, and placements that invoice from day one.
Intake & Review Ops
AI drafts, qualified reviewers approve, and exceptions route to a person by design.
Model-Risk & Compliance Ops
Regulated workflow agents with the testing evidence your reviewers need.
Need the team as well as the system?
QB Works helps you hire and run technology teams: dedicated teams, managed pods, a training academy, and hiring for key roles.
Capability Centers
Your own dedicated team, set up and run for you.
Managed Talent Pods
Small, assessed teams working in your hours.
The Academy
Engineers trained on your stack, with verified portfolios.
Precision Hiring
Fixed-fee search with an agreed timeline.
How we assess the people we introduce.
Credentials only tell part of the story. Engineers and specialists go through a six-step assessment of their real work before we introduce them, and you can see the assessment report.
Curated intake
Referral, verified track record and targeted search.
Structured assessment
Role-specific and scored against a rubric agreed in advance.
Real-work review
Production work reviewed by a senior practitioner in the same field.
Expert assessment
An in-depth, practical assessment led by a senior practitioner.
Human approval
An experienced consultant validates every match against your context.
Performance feedback
Delivery outcomes are recorded and inform every future match.
Examples of our work.
Representative examples, shared without client names. We are happy to talk through the details on a call.
Five disconnected tools became one system. Placements now invoice the day a consultant starts, and utilization is visible by project in real time.
Two weeks before launch, independent red-teaming identified high-severity failures, including a source-document leak through indirect prompt injection.
A two-person pod took a document-intake workflow from mission brief to a production agent with guardrails, with every milestone visible to the client throughout.
Guides and templates.
In-depth research, step-by-step playbooks and working templates, free to read and use.
A Failure Taxonomy for Production Agents
A structured taxonomy of how LLM agents fail in production, drawn from OWASP, NIST, MITRE, Microsoft and academic research, with detection and severity guidance.
Read the research →From Pilot Purgatory to Production: A Step-by-Step Route for AI Pilots
A practical route for the AI pilot that has not shipped: diagnose the stall, pick the workflow, set the accuracy bar, stage the rollout and operate it.
Read the playbook →The AI Vendor Question Set: 20 Questions That Reveal Delivery Capability
Twenty questions for AI vendor selection across delivery, evaluation, security, operations, and exit, with strong answers and red flags for each.
Use the template →Work with us.
We work with engineers and domain specialists who build and evaluate AI systems. Remote-friendly, full-time or per project.
- Production AI EngineerEngineering
- AI Evaluation SpecialistAssurance
- Domain ReviewerExpert network
- Delivery LeadCore team
Two offices. One team across time zones.
Serving clients from Asia-Pacific to Europe and the Americas, with working-hours overlap agreed up front.
Bring the pilot you want live, the agent you need to trust, or the role you need to fill.
Start with a 30-minute call. If we are a good fit, we follow up with a written scope and a price range.