TeqEngine builds production agentic AI systems: workflow architecture, evals, MCP/tool integration, secure auth, and the platform layer agents need to operate against real business systems.
Readiness Engine
Readiness
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Evals pass
0%
Coverage
0%
Risk register · 3 open
Decision
evaluating
Public proof, real stakes
Operating stack in production
Built with
Competitive Difference
Models keep getting better. The hard part is everything around them — the workflow architecture, eval harnesses, MCP and tool boundaries, auth and observability, and the rollout discipline that lets agents run against real business systems without breaking.
What teams are tired of
Assessments that never turn into working systems
Agent demos without evals, permissions, observability, or rollback
Large-firm delivery models that pitch with seniors and execute with juniors
A principal engineer owns the architecture and delivery path
AI work is treated as product, platform, security, and operations together
The first engagement produces a build decision, not another strategy deck
Operating stack
TeqEngine Method
A repeatable operating model for moving agentic AI from promising workflow to production system. It creates the decisions, gates, and technical artifacts your team needs before committing to a build.
Separate durable AI opportunities from demos, search, workflow, and no-build cases.
Define what agents can call, when they escalate, and how failures resolve.
Map permissions, identity context, sensitive data paths, and review surfaces.
Measure correctness, cost, latency, drift, and regression before rollout.
Sequence pilots, rollback paths, observability, and human approvals.
Output
A build, buy, kill, or sequence decision with enough architecture detail to move directly into execution.
01
Workflow
02
Harness
03
Platform
Most teams start with the Diagnostic and move down the ladder as the work proves out: decide what to build, build it, deploy it behind real rollout discipline, then keep it reliable in production.
A focused review that scores use-case fit, architecture, data, security, evals, and delivery ownership — and ends in a clear go/no-go.
Production agent systems with MCP/tool boundaries, auth, evals, observability, and human escalation — engineered to survive real users.
Senior-builder-led embedment to harden, integrate, test, and ship the system into production behind real rollout discipline — not a slide describing one.
Ongoing governance for teams running AI in production: recurring evals, release gates, incident review, and architecture oversight as the system evolves.
What We Do
We work where models meet product, data, cloud, security, and operations. The output is not an AI demo. It is working software with gates, telemetry, ownership, and a path to scale.
How We Deliver
Principal-led architecture, clear governance, and a relentless focus on shipping production-ready systems. The process is built to create decisions, working code, and operational ownership — with senior bench capacity scaled to engagement scope.
Architecture and ownership stay with the principal. A vetted senior bench scales execution to engagement scope. No junior handoffs, no consultancy theater.
Weekly exec updates, risk tracking, architecture reviews. Clear milestones and transparent roadmaps.
Code reviews, automated testing, CI/CD pipelines. AI-specific evals, red teaming, and telemetry.
Ship working software every sprint. Continuous feedback loops and rapid iteration on what matters.
Every engagement produces concrete engineering artifacts — architecture sketches, risk maps, eval plans, and deployment checklists — so your procurement, security, and engineering teams can evaluate the work before a single line of production code is committed.
Readiness scorecard, executive memo, architecture sketch, risk map, eval plan, and implementation roadmap.
Current workflow, AI-assisted target workflow, human review points, system handoffs, and ownership changes.
Data, model, retrieval, tool, permission, hallucination, latency, cost, security, and rollout risks.
Evals, traces, monitoring, rollback, access control, incident paths, support model, and launch gates.
These questions turn AI ambition into an accountable delivery plan before budget, security review, or launch timing gets locked.
The same checklist we run inside a paid diagnostic — the questions that decide whether an AI workflow, agent, or AI-coded product is ready for real business use, or one incident away from a problem.
Latest essays
A recurring essay series on the systems work behind production agents — eval harnesses, MCP boundaries, observability, and the rollout patterns that keep AI from failing under real load.
Vertical scaling buys time, not a future. Query optimization, caching, read replicas, and a clear-eyed look at when sharding is actually worth its complexity.
Transportation Management Systems quietly decide a logistics operation's margins. Where the savings really come from — routing, rate shopping, and the architecture that holds at scale.
Running distributed engineering teams without a full-time seat: the communication, time-zone, and process realities that decide whether the work ships.
A principal-led engagement to inspect your architecture, agent workflow, evals, auth boundaries, and delivery risk before you commit to a larger build.
We'll map your system, identify risks, and deliver a production-ready plan you can act on immediately.