Who owns the work?
Users, operators, specialists, decision-makers, approvers, and the people accountable for the outcome.
0.1 Labs · Forward-Deployed AI Engineering
Understand the mission.
Engineer into the work.
0.1 Labs helps organizations move from AI experimentation to operating capability. We work inside real workflows, connect intelligence to the systems and knowledge that matter, engineer the deployment, govern what it can do, and measure whether it works.
Useful before impressive. Measured before scaled.
Mission Before Model
Don't begin with “Which AI?”
Begin with “What should work better?”
A deployment should begin with the operating problem: where skilled people lose time, where knowledge becomes difficult to retrieve, where handoffs fail, where repetitive work accumulates, or where decisions lack the context they need.
Only after the mission and workflow are understood do we determine whether the right intervention is an agent, conventional automation, retrieval, an AI-native application, decision support, or something else entirely.
The technology follows the problem. The architecture follows the environment. The deployment is judged by the outcome.
The Operational Map
Five layers before
we engineer.
Before connecting a model to an organization, we map the environment in which the resulting system will have to operate.
What actually happens?
Steps, handoffs, queues, exceptions, repetitive work, bottlenecks, and points where judgment enters the process.
What must be known?
Documents, policies, records, institutional memory, external evidence, and specialist context required to do the work well.
Where does work live?
Applications, APIs, databases, repositories, communication systems, tools, and existing automation.
What may AI do?
Identity, permissions, data boundaries, approvals, prohibited actions, escalation, monitoring, and accountability.
What We Build
Intelligence connected
to the operation.
Architecture follows the mission. We use AI where intelligence adds value and conventional software or automation where it does not.
AI Agents & Digital Coworkers
Bounded systems that reason over context, use approved tools, perform multi-step work, maintain state, and escalate appropriately.
AI-Accelerated Engineering
Agentic workflows for software creation, testing, debugging, modernization, migration, documentation, review, and engineering knowledge.
Enterprise Knowledge Systems
Governed retrieval and synthesis across policies, documents, repositories, institutional memory, and specialist knowledge.
Intelligent Automation
AI combined with deterministic systems where interpretation, classification, extraction, synthesis, or reasoning creates measurable value.
AI-Native Internal Products
Purpose-built applications that place intelligence inside the workflow instead of forcing employees to work around a generic chat interface.
Decision Support Systems
Systems that assemble context, retrieve evidence, compare information, identify inconsistencies, and support accountable human judgment.
Production Architecture
The model is not
the whole system.
Production AI is the interaction between intelligence, organizational context, tools, identity, permissions, evaluation, observability, and human control.
Intelligence
Frontier and specialized models selected according to capability, economics, latency, privacy, and reliability.
Context
Enterprise data, documents, policies, history, applications, business rules, and task-specific knowledge.
Tools
APIs, databases, repositories, business applications, MCP-connected systems, and approved operational actions.
Control
Identity, least privilege, approvals, evaluation, monitoring, auditability, cost controls, and escalation.
HUMAN OPERATORS
↓
AI SYSTEM / AGENT
↓
INTELLIGENCE
Models
Models
CONTEXT
Knowledge
Knowledge
TOOLS
APIs · MCP · Systems
APIs · MCP · Systems
↓
IDENTITY · PERMISSIONS · EVALUATION · OBSERVABILITY · HUMAN APPROVAL
Govern the Agent
Treat autonomy
as a permission.
An AI agent should not inherit unlimited access simply because it can perform a task. Production systems require explicit authority boundaries.
Illustrative permission model. Actual controls depend on the workflow, systems involved, risk profile, and deployment environment.
Engagement Model
Start bounded.
Expand on evidence.
Engagements are scoped to the workflow, systems, security requirements, deployment environment, and operating objective. We do not publish a one-size-fits-all engineering rate.
AI Readiness Review
Map the operating environment, identify candidate workflows, review readiness and define the strongest first deployment.
Scoped engagementForward Deployment Sprint
Put engineering against one bounded operational problem and move from discovery toward a working system.
Scoped engagementProduction Build
Engineer, integrate, evaluate, secure, document, and deploy a validated AI capability.
Custom scopeEmbedded Engineering
Maintain forward-deployed engineering alongside the organization as systems evolve through real operational use.
Ongoing engagementEnterprise Deployment
Extend proven architecture across workflows, teams, systems, agents, governance structures, and operating units.
Custom engagementAlready Have a Problem to Solve?
Bring us the workflow.
Tell us what should work better, what systems are involved, and what constraints matter. We'll determine whether a forward-deployed engagement is appropriate.
Discuss a Deployment →Measured Before Scaled
Deployment is not
the success metric.
Before deployment, define what improvement means. After deployment, measure whether the system actually changed the operation.
Did the workflow become materially faster?
Did output quality or consistency improve?
Did preventable correction and repetition decline?
Where are people still required, and is their time being used better?
Do the operating benefits justify model, infrastructure, engineering, and maintenance costs?
Can people trust the system enough to use it consistently in real work?
If it does not materially improve the workflow, we improve it—or stop.
The Production Loop
Production generates knowledge
planning cannot.
Real users, exceptions, system behavior, economics, and failure modes create information that cannot be fully discovered in a conference room.
DEPLOY
→
OBSERVE
→
LEARN
→
ENGINEER
→
DEPLOY ↺
The 0.1 Labs Standard
No AI theater.
We would rather deploy a modest system that demonstrably improves the work than an ambitious demonstration that cannot survive contact with production.
Solve a recognizable operating problem before optimizing for how sophisticated the demonstration appears.
Expansion follows evidence of usefulness, reliability, adoption, governance, and acceptable economics.
AI may perform increasingly sophisticated work. Responsibility for consequential outcomes remains defined within the organization.
0.1 Labs
Start with the problem.
Build toward the outcome.
Whether you are still evaluating where AI belongs or already have a workflow that needs to work better, 0.1 Labs starts with the operating environment and works toward a measurable, governed deployment.
Forward-Deployed AI Engineering0.1 Labs is a practice of Federal Review Group. Engagement scope and availability depend on technical requirements, security and governance considerations, systems involved, and deployment environment. Third-party model, cloud, software, and infrastructure costs are separate unless included in the agreed scope.