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Forward-deployed AI engineering

Forward-deployed engineers who build your AI agents, then run them.

We embed with your team to build AI products and agents, then stay on to run, evaluate and improve them.

  • Model-independent
  • Inside your repositories
  • Built, then run monthly
Discuss your projectSee our work
#1 on the App StoreBuilt the backend and infrastructure powering the app.CTO through acquisitionBuilt the healthcare platform acquired by an a16z-backed company.100M+ messagesHandled in a single day.50+ MCP tools builtIn an agent-native platform.

The system behind the AI product

  1. Product experience
  2. Agent & tool layer
  3. Evaluation & release
  4. Platform foundations
Our engineering scope, from the interface to the underlying systems.

Ways to work together

Start with the work in front of you.

Build and run an AI product with us, or bring us into your team’s coding-agent workflow. Reviews, diligence and fractional CTO work stand on their own or lead into either.

How forward-deployed engagements work

Build, then run

Build an AI product or agent

We embed with your team, build the agent platform or AI product, then stay on a monthly retainer to run, evaluate and improve it.

Initial three-month engagement · then a monthly retainer

Your coding-agent workflow

Make your team's coding agents ship

We work inside your engineering workflow to ship features, strengthen verification and improve how your engineers use coding agents.

Start with a harness review or ship features directly

Architecture & Harness Review

Two to three weeks

Technical & AI diligence

Two to four weeks

Fractional CTO

Initial three-month term

Engineering capabilities

The product. The intelligence. The systems underneath.

Architecture, implementation and verification for the product, agent runtime and systems it depends on.

Agent systems & product engineering

  • Workflow architecture & MCP tools
  • Product interfaces & secure integrations
  • Model routing & human escalation
Explore agentic systems

Evals, retrieval & reliability

  • Golden paths, adversarial cases & release gates
  • Retrieval diagnostics & trace capture
  • Cost controls, monitoring & recovery
Explore evals & reliability

Platform, data & cloud foundations

  • Data architecture, pipelines & quality checks
  • Identity, permissions & audit logging
  • Infrastructure, deployment & rollback
Explore platform foundations

Harness engineering

Inspectable work. Clear ownership.

The harness is everything around a coding agent: repository context, bounded tools, permissions and verification gates. Agents implement inside it. Engineers own the architecture, acceptance and release.

Harness engineering, the method Delivery & ownership

Sample review · synthetic example

Tenant-scoped customer export

How a review finding reads: what we observed, the evidence behind it and the check that decides it.

Finding
The export returns only active customers from the caller’s own tenant, after the administrator role and export permission are checked.
Evidence
24 tests passed. A deliberately unscoped version returned another tenant’s row, and the exact-membership check caught it.
AllowedGET /customers/export

Administrator with export permission · HTTP 200 · tenant A only

customer_id,name,email
a-101,"North, Branch",north@example.test
RejectedGET /customers/export?tenant=b

Refused with HTTP 400: the export takes no query parameters. Identity headers sent by the client are ignored; the session decides the tenant.

Acceptance check
Exact membership and fields, a second tenant, active-only rows, and no partial download when bounds are exceeded or the database fails.
Next decision
Verify integration with your identity system and tenant model. Owner: the receiving engineering team.
Read the full sample review PDF · 1 pageRun the worked example

Delivery, in writing

A weekly status and decision log.

Progress, risks, tradeoffs and the decisions needed from your team, in writing.

See a sample decision record

Security & vendor review

Ready for your security review.

Questionnaires, architecture diagrams and control summaries, prepared as part of the engagement so procurement moves as fast as the build.

Review our security approach
Daniel Voigt
TeqEngineLa Jolla, California

The firm

Engineering experience, carried into what comes next.

TeqEngine is a forward-deployed AI engineering firm. We build agent platforms, AI products and the cloud and data systems behind them inside our clients’ teams, then stay on to run what we build.

Daniel Voigt

Founder & Principal Engineer

Daniel Voigt, founder of TeqEngine: AI-native engineering, healthcare CTO experience, agent platforms and large-scale application architecture.

Healthcare leadership
CTO of AZZLY, June 2022–December 2025, through acquisition.
Education
Kenyon College · Stanford Engineering (AI Certificate)
Daniel’s backgroundAbout TeqEngine

Practical resources

Take a closer look.

The same questions we use in our reviews, open to read.

Launch readiness

Before an agent goes live.

A practical checklist for permissions, evals, observability, recovery and ownership.

Read the launch checklist

Codebase inspection

What to inspect in an agent-built codebase.

The questions we ask when we review a codebase written with coding agents: behavior, permissions, maintainability and handoff.

Read the inspection guide

Worked example

A customer export, built through the harness.

The contract, the permissions, the exact output and the tests behind it. Python, no dependencies.

Open the worked example

Start a conversation

What are you building—or deciding?

Tell us where you are. We reply within one business day, and the first conversation ends with a recommended next step.

Discuss your project