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AI Workforce Factory

AI agents that know your company and work inside your cloud.

We help engineering teams put AI agents into production workflows, connect them to company knowledge, and keep engineers in control of delivery.

Book a 30-min call with the founders Tell us your problem

For platform teams, engineering leaders and companies moving beyond an AI pilot.

We work with AWS, Google Cloud, Azure and Oracle Cloud (OCI).

Engineers approve what matters
  1. Intent
  2. Agents
  3. Gates
  4. Production

Fixes and feedback flow back to the agents

From an incident to a reviewed change.

See how company context, an agent and an engineer come together in one workflow.

Illustrative workflow

Service health alert → investigated cause → reviewed change

Reviewed outcomes inform the next investigation1DetectService health alertA scoped investigation2Gather contextLogs, runbooks and historyEvidence linked to its sources3Propose a fixA reasoned change proposalRisk and rollback plan4Engineer reviewA person approves or revisesWithin agreed permissions5ValidateTests and service healthFailed checks stop the workflow6RecordOutcome and decision trailInform the next investigationShared memoryEvidence, decisions and lessons, linked for the next investigation and runbook update.Reviewed outcomes inform the next investigation1DetectService health alertA scoped investigation2Gather contextLogs, runbooks and historyEvidence linked to its sources3Propose a fixA reasoned change proposalRisk and rollback plan4Engineer reviewA person approves or revisesWithin agreed permissions5ValidateTests and service healthFailed checks stop the workflow6RecordOutcome and decision trailInform the next investigationShared memoryEvidence, decisions and lessons.Reviewed outcomes improvethe next investigation and runbook.
Explore the six steps

  1. Detect

    An alert becomes a scoped investigation.

    See the details

    Input · incident alert

    A service health alert identifies the affected workload. The incident defines what the agent may investigate and which engineer owns the response.

  2. Gather context

    Logs, runbooks and relevant history, together.

    See the details

    Context · linked evidence

    The agent gathers the permitted logs, recent deployment changes and related runbook entries. Each finding keeps a link to its source, so the engineer can check the reasoning.

  3. Propose a fix

    A proposed change with a reason and rollback plan.

    See the details

    Output · change proposal

    A recent configuration change is a possible cause. The agent proposes reverting it, explains the evidence and describes the risk and rollback path. A proposal is ready for review, not a production action.

  4. Engineer review

    A person decides whether the change should proceed.

    See the decision

    Decision · approval or revision

    The engineer reviews the source evidence and proposed change. They can approve, reject or ask for more investigation. The workflow only continues under the permissions agreed for the pilot.

  5. Validate

    Checks decide whether the result is acceptable.

    See the details

    Check · tests and service health

    The approved change passes the agreed tests and deployment checks. The service is observed against its health criteria; a failed check stops the workflow and returns it for investigation.

  6. Record

    Keep the outcome and its decision trail.

    See the details

    Record · incident summary

    The incident record links the evidence, proposal, reviewer decision and validation result. The reviewed outcome can inform the next investigation and updates to the runbook.

An example of a workflow we can scope with you. This is an illustration, not a customer incident or a live product demo.

Bring one runbook or recurring incident. We’ll discuss where an agent could help and what a useful pilot would need to prove.

Explore AI SRE & on-callTell us your problem

Live website example

An assistant finds a service and prepares an inquiry.

“We need help with incident response and on-call reliability.”

  1. Discover the available tools.
  2. Read the published SRE offering.
  3. Prepare a brief for you to review.

This scripted demonstration calls our real public MCP endpoint. It uses a fixed example, without model inference. Inquiry preparation sends no email and stores no contact details.

What is slowing your team down?

Choose the problem you want to solve. Start with a scoped service engagement, with clear deliverables before you commit to a wider rollout.

Scope, timeline and commercial terms are agreed before work starts. Bring your constraints to the first conversation; we’ll choose the smallest useful engagement together.

Engineering services you can start with today.

Work with our engineers on your cloud, reliability, security, costs or AI delivery. The products supporting this work are listed afterward, with their current availability.

Available services

Products · current availability

  1. Sternguard

    Cloud governance and AI SecOps: guardrails in Git, enforced across your cloud organisation, with risky-access containment and compliance evidence. AWS today. Google Cloud, Azure and Oracle Cloud next.

    • Early access

  2. Shiftyard + YardBridge

    Shiftyard is an open-source software factory for coding agents, in pre-release: governed delivery on your own machines. YardBridge, its paid organisation layer, is in early access.

    • Shiftyard: Open source · pre-release
    • YardBridge: Early access

  3. Kanerva

    A second brain for people and AI agents. Ingest the knowledge you connect or upload, keep it with its sources, and use the same memory across tools and sessions. Coming soon.

    • Open source · coming soon

  4. Tillerpin

    A System-1 model for fast, typed decisions (yes/no, choice and relations), designed for calibrated decision-making in agent pipelines, with open weights and documented training data. Coming soon.

    • Coming soon

One operating model, from pilot to production.

  1. Assess

    We start in your cloud account, from the outcome you need and the constraints you have.

  2. Build under controls and evals

    Governance controls and evaluations decide what ships, with a human approving what matters.

  3. Run and improve

    We stay accountable after go-live and keep improving what we built.

Explore the architecture and delivery principles

  1. Intent

    Humans set the intent and the policy agents work within.

  2. Agents

    Agents do the work, with every step traced for review.

  3. Gates

    Every change is validated; a failed check goes back to the agents.

  4. Production

    An engineer approves and releases the accepted result.

  5. Day 2 operations

    Self-healing DevOps, SecOps and FinOps. Customer bug reports and feedback loops.

Humans People watch and steer every stage. Nothing ships unreviewed.

Your cloud, your data, your rules.

  • Designed to run in your account
  • Human approval where it matters
  • Open source and open weights where we can
  • Evaluations decide what ships

Built by engineers, accountable for results.

Founders

  • Anton Grishko Co-founder · Product & Engineering

    Anton has built and run infrastructure since 2005 and has worked as a DevOps architect for over ten years. In the Google Cloud unit of a large IT services firm, he led complex migrations of large enterprises to Google Cloud and spoke at Google events. Before that he led DevOps teams for enterprise and product companies, and he went on to serve as a chief architect. At F200.ai he leads product and engineering.

  • Ivan Hilevych Co-founder · Business Development

    Ivan has spent over ten years helping tech startups and growing companies find engineering teams that deliver. He has led sales for R&D and DevOps services companies as Chief Sales Officer and Head of Sales, and managed strategic enterprise accounts at a Microsoft partner. He works relationship-first: an honest conversation about fit comes before any deal. At F200.ai he leads business development.

The team

We have run production infrastructure since the mid-2000s, from telecom platforms and enterprise data centres to multi-cloud estates on AWS, Google Cloud and Azure. Between us: large enterprise migrations to Google Cloud, a cross-cloud migration of hundreds of microservices, DevSecOps and secure-SDLC programmes, SRE practices and cloud cost optimisation for retail, fintech, real-estate and enterprise software companies. More recently we have worked on GenAI solution architecture (RAG and knowledge bases, evaluation frameworks, guardrails and security for LLM applications) and agentic software delivery with human-in-the-loop controls, which is the practice behind our software factory. On the commercial side, we bring over ten years of building delivery partnerships with startups and growing companies.

Book a 30-min call with the founders

Built on the tools your team already uses.

We work across cloud platforms, delivery pipelines, observability and AI tooling. Explore the stack when you need the technical detail.

Explore our engineering stack

Clouds

  • AWS
  • Google Cloud
  • Azure
  • Oracle Cloud (OCI)

Platform and infrastructure as code

  • Kubernetes
  • Terraform
  • OpenTofu
  • Terragrunt
  • Helm

GitOps and delivery

  • Argo CD
  • GitHub Actions

Security and secrets

  • Istio
  • HashiCorp Vault
  • Trivy

Observability

  • Prometheus
  • Grafana
  • OpenTelemetry

Data

  • PostgreSQL
  • Redis
  • Apache Kafka
  • ClickHouse
  • OpenSearch

AI services and models

  • Amazon Bedrock
  • Google Gemini
  • Anthropic Claude
  • OpenAI
  • Hugging Face
  • PyTorch
  • Model Context Protocol

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