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Forward-deployed AI engineering

AI agents built with your team, and ready for every new model.

Forward-deployed engineers build your AI features and agents inside your systems, measure them on quality, speed, cost, control and drift, and keep them current through every model release. No model to sell. You keep everything.

  • No model to sell
  • Inside your repositories
  • Measured on every model release
Discuss your projectReview an agent
#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

Where are you starting?

Every engagement runs on the same method. The first step depends on where your agents are today.

How forward-deployed engagements work

Build

Building something new

Add an AI feature, connect it to your systems, or build an agent workflow, measured from the first week.

Forward-deployed build · initial three-month engagement

Review

Agents already live

Find where the time and money go, score the agent on five measures and fix what matters first.

Harness Review · two to three weeks

Upgrade

A model change coming

Decide adopt, hold or switch on your own tasks, with a regression gate and a way back.

Upgrade Sprint · two to three weeks

Design

Not sure an agent fits yet

Choose the workflow worth automating, compare the ways to do it and prove a first version.

Harness Design · three weeks

Technical & AI diligence

Two to four weeks

Fractional CTO

Initial three-month term

Why TeqEngine

Forward-deployed engineers for the agents your business runs on.

Built by engineers who have run agent platforms, measured across every layer, and owned by your team when the work is done.

How we compare with the alternatives

  1. We've built it and run it.

    We built the backend AI, APIs and agent tooling behind a platform used by Fortune 500 enterprises and a unicorn startup, with 50+ MCP tools across Anthropic, OpenAI and Google models. We also led engineering for a regulated healthcare platform through its acquisition by an a16z-backed company. We review agents the way people who have had to run them do.

  2. The whole system, not just the evals.

    Interface, services, orchestration, tools, models and settings, plus memory and human oversight, measured on quality, speed, cost, control and drift. Most slow or wrong answers come from somewhere a single eval never looks.

  3. No model to sell.

    We don't resell models or platforms. We test your work across providers and recommend what your evidence supports: a different model, the platform you already pay for, simpler automation, or no agent at all.

  4. You keep everything when we're done.

    Code, evals, test sets, dashboards and runbooks live in your repository. The work is finished when your team can ship the next model change without us. Keep us on monthly when you want us to run it.

  5. Ready for the next model.

    Harness Watch reruns your scorecard on each relevant release and tells you whether to adopt, hold or switch, what you can now delete, and when your agents have earned more autonomy.

The Harness Method

Six questions. Five measures. Evidence at every step.

Every engagement runs on the same method. We ask what the work must achieve, what it may touch and how we’ll know, then measure quality, speed, cost, control and drift on your own cases.

Delegate the implementation. Engineer the judgment. Own the result.

The Harness 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

Harness Watch

Every model release, a verdict.

Adopt, hold or switch, with the evidence, and what the new model lets you delete.

How Harness Watch works
Daniel Voigt
TeqEngineLa Jolla, California

The firm

Engineering experience, carried into what comes next.

TeqEngine is a forward-deployed AI engineering firm. We build AI agents and products inside our clients’ teams, measure them with the Harness Method, and keep them current through every model change.

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.

Model upgrade

Before you switch models.

Baseline, candidates, regression gate, rollout and cleanup for the model behind an agent.

Read the upgrade checklist

Launch readiness

Before an agent goes live.

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

Read the launch checklist

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 what you’re building, what is getting in the way, and the outcome you need.

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