# TeqEngine > Forward-deployed AI engineering for teams shipping agents and AI products. TeqEngine embeds engineers with a client's team, builds the agent platform or AI product, sets up or strengthens the harness the client's engineers use with coding agents, and runs what it builds on a monthly retainer. TeqEngine is based in La Jolla, California, serving clients remotely. The canonical website is https://teqengine.ai/. The former teqengine.com domain redirects to this site. Daniel Voigt is TeqEngine’s founder and principal engineer. TeqEngine works with healthcare companies, fintech and financial-services teams, software engineering organizations adopting coding agents, investors and acquirers, and agencies that need a specialist agent engineering partner. ## Company and services - [Company overview](https://teqengine.ai/): Forward-deployed AI engineering for agent platforms and AI products. - [Careers](https://teqengine.ai/careers): Introductions for lead and senior forward-deployed engineers with AI-native, full-stack experience, plus technical project management and sales development under consideration. - [Forward-deployed engineering](https://teqengine.ai/services/forward-deployed-engineering): How engagements run: an architecture review, a forward-deployed build, then a monthly retainer to run and improve what was built. - [AI agent development](https://teqengine.ai/services/agentic-ai-systems): Workflow architecture, MCP tools, authorization and rollout. - [Evals and reliability](https://teqengine.ai/services/evals-and-reliability): Evaluation suites, observability, retrieval diagnostics and release gates. - [Platform foundations](https://teqengine.ai/services/platform-foundations): Cloud, data and application infrastructure. - [Architecture & Harness Review](https://teqengine.ai/diagnostic): A two-to-three-week review of an AI system or a team's coding-agent workflow, ending in a prioritized plan. - [Technical & AI diligence](https://teqengine.ai/for/investors): Diligence for investors and acquirers, including agent-built codebases. - [Fractional CTO](https://teqengine.ai/services/fractional-cto): A senior technical counterpart for architecture, agent adoption and board decisions. - [Delivery model](https://teqengine.ai/about/delivery-model): How a forward-deployed team works inside the client's team, from first increment to monthly operations. - [Trust and security](https://teqengine.ai/trust): Security practices, data handling and security-review support. - [Daniel Voigt](https://teqengine.ai/daniel-voigt): Founder biography. - [Contact](https://teqengine.ai/contact): Discuss a project or review. ## Who we work with - [Healthcare](https://teqengine.ai/for/healthcare): Forward-deployed engineers who build agents and AI features for healthcare products, from EHR and revenue-cycle data to approvals and audit trails. - [Fintech & financial services](https://teqengine.ai/for/financial-services): Forward-deployed engineers who build agents for fintech and financial services, with per-action authorization, idempotent tools and a full audit trail. - [Engineering teams](https://teqengine.ai/for/engineering-teams): Forward-deployed engineers who set up or strengthen your coding-agent harness: context, permissions, verification gates and review, proven on real features. - [Investors](https://teqengine.ai/for/investors): Technical and AI diligence for investors and acquirers: evaluate the real AI system, inspect agent-built codebases and get an integration and remediation plan. - [Agencies & partners](https://teqengine.ai/for/partners): A forward-deployed agent engineering partner for agencies, consultancies and platform vendors: agents, MCP tools and evaluations under your engagement. ## Published engineering evidence - [Agent Platform Engineering for Enterprise AI](https://teqengine.ai/insights/making-a-content-platform-agent-native): Agent platform engineering across backend AI, APIs and infrastructure. Delivery includes 50+ MCP tools built, semantic search, and integrations with Anthropic, OpenAI and Google for a platform serving a unicorn startup and Fortune 500 companies. - [Consumer platform](https://teqengine.ai/case-studies/app-store-platform): Backend and infrastructure for an app that reached #1 on the App Store, with more than 100 million messages handled in a single day. - [Healthcare platform](https://teqengine.ai/case-studies/healthcare-ehr-platform): CTO leadership, software development and technical diligence for an EHR and revenue-cycle platform through its acquisition by an a16z-backed company. ## Reading paths - [AI architecture and integration](https://teqengine.ai/insights/topics/architecture): Choose the control model, integration boundary and evidence path for an AI product. A curated reading path for CTOs and engineering teams. - [AI reliability and security](https://teqengine.ai/insights/topics/reliability): Evaluation, authorization, observability and recovery for live AI systems. Guides, a sample planner and reproducible engineering examples. - [AI investment and vendor decisions](https://teqengine.ai/insights/topics/investment): Compare AI development economics, sourcing choices and vendor evidence. Practical guides and private browser worksheets for founders and engineering leaders. ## Decision guides - [How to choose an AI agent development company](https://teqengine.ai/insights/choosing-ai-agent-development-company): An evidence-based vendor scorecard for CTOs and founders: compare architecture, evaluation, security, delivery and the system you will own. [Markdown](https://teqengine.ai/insights/choosing-ai-agent-development-company/markdown). - [AI agent development cost: a practical planning model](https://teqengine.ai/insights/ai-agent-development-cost): Model the scope, operating costs and cost per successful task of an AI agent. Includes a transparent calculator and a worksheet for comparing proposals. [Markdown](https://teqengine.ai/insights/ai-agent-development-cost/markdown). - [Build vs. buy AI agents: decide what you need to own](https://teqengine.ai/insights/build-vs-buy-ai-agents): A layer-by-layer framework for enterprise AI agent decisions, with a comparison matrix, pilot acceptance criteria and an exit test. [Markdown](https://teqengine.ai/insights/build-vs-buy-ai-agents/markdown). - [AI agent evaluation: a release framework](https://teqengine.ai/insights/ai-agent-evaluation-framework): Define task success, test permissions and side effects, evaluate failure slices, and turn agent evaluations into an inspectable release decision. [Markdown](https://teqengine.ai/insights/ai-agent-evaluation-framework/markdown). - [MCP server security: a review checklist](https://teqengine.ai/insights/mcp-server-security): A practical MCP security review covering identity, authorization, tool execution, prompt injection, state handles and evidence for a release. [Markdown](https://teqengine.ai/insights/mcp-server-security/markdown). - [AI technical due diligence: what the evidence needs to show](https://teqengine.ai/insights/ai-technical-due-diligence): A technical diligence framework for AI software investments and vendor decisions: request the right evidence, test material claims and prioritize findings. [Markdown](https://teqengine.ai/insights/ai-technical-due-diligence/markdown). - [AI agents vs. workflows: choosing the right control model](https://teqengine.ai/insights/ai-agents-vs-workflows): Use a workflow when the path is known. Introduce an agent where runtime judgment earns its complexity—and keep consequential actions under explicit control. [Markdown](https://teqengine.ai/insights/ai-agents-vs-workflows/markdown). - [AI agent architecture: a reference design](https://teqengine.ai/insights/ai-agent-architecture): A reference architecture for identity, state, tool execution, permissions, evaluation and recovery—with a concrete workflow a CTO can inspect. [Markdown](https://teqengine.ai/insights/ai-agent-architecture/markdown). - [Integrating AI into an existing enterprise platform](https://teqengine.ai/insights/enterprise-ai-integration): Add AI capabilities through existing identity, domain APIs and data contracts. Plan the migration, rollout and recovery before introducing autonomous writes. [Markdown](https://teqengine.ai/insights/enterprise-ai-integration/markdown). - [MCP vs. APIs: where each belongs](https://teqengine.ai/insights/mcp-vs-api): Compare MCP and direct APIs for discovery, domain behavior, permissions and execution. Decide when a shared protocol interface is useful. [Markdown](https://teqengine.ai/insights/mcp-vs-api/markdown). - [Single agent vs. multi-agent systems](https://teqengine.ai/insights/single-agent-vs-multi-agent): Compare single-agent and multi-agent designs through task evidence, coordination cost, context handoff and responsibility for the final action. [Markdown](https://teqengine.ai/insights/single-agent-vs-multi-agent/markdown). - [RAG vs. fine-tuning: diagnose the missing capability](https://teqengine.ai/insights/rag-vs-fine-tuning): Choose retrieval or fine-tuning by diagnosing missing knowledge and behavior. Compare freshness, access, evaluation evidence and operating requirements. [Markdown](https://teqengine.ai/insights/rag-vs-fine-tuning/markdown). - [Taking an AI prototype to launch](https://teqengine.ai/insights/ai-launch-readiness): Turn a useful demo into an operable product with explicit task outcomes, evaluation, rollout gates, recovery and ownership. [Markdown](https://teqengine.ai/insights/ai-launch-readiness/markdown). - [AI agent observability: from traces to decisions](https://teqengine.ai/insights/ai-agent-observability): Trace task outcomes, tool actions and waiting states to explain failures, control cost and investigate incidents while limiting sensitive data collection. [Markdown](https://teqengine.ai/insights/ai-agent-observability/markdown). - [RAG evaluation: test retrieval, answers and access](https://teqengine.ai/insights/rag-evaluation): Separate retrieval quality, answer support and permission failures. Use a small worked corpus to understand what each metric establishes—and what it misses. [Markdown](https://teqengine.ai/insights/rag-evaluation/markdown). - [LLM as a judge: calibration before automation](https://teqengine.ai/insights/llm-as-a-judge): Use model graders for tasks they can judge reliably. Calibrate against reviewed examples, inspect missed failures and keep critical release rules explicit. [Markdown](https://teqengine.ai/insights/llm-as-a-judge/markdown). - [LLM model routing and fallback](https://teqengine.ai/insights/llm-model-routing): Choose model routes by task evidence, data policy and operating limits. Treat fallback as a tested execution path—not permission to send any workload anywhere. [Markdown](https://teqengine.ai/insights/llm-model-routing/markdown). - [LLM latency optimization for useful completion](https://teqengine.ai/insights/llm-latency-optimization): Locate delays across queues, models, tools and approval. Compare latency changes against task quality, cost and reliable completion. [Markdown](https://teqengine.ai/insights/llm-latency-optimization/markdown). - [AI agent authorization at the action boundary](https://teqengine.ai/insights/ai-agent-authorization): Define who can do what to which resource, derive identity from trusted context and recheck authority when an agent action executes. [Markdown](https://teqengine.ai/insights/ai-agent-authorization/markdown). - [Prompt injection defenses for tool-using agents](https://teqengine.ai/insights/prompt-injection-defenses): Review prompt injection defenses at the data and tool boundaries, including scoped access, output validation, approval and adversarial tests. [Markdown](https://teqengine.ai/insights/prompt-injection-defenses/markdown). - [Human approval workflows for AI agents](https://teqengine.ai/insights/human-approval-ai-agents): Bind human approval to a specific AI agent action. Review scope, expiry, changed state and the authorization needed before execution. [Markdown](https://teqengine.ai/insights/human-approval-ai-agents/markdown). - [Permission-aware RAG: access before retrieval](https://teqengine.ai/insights/permission-aware-rag): Preserve tenant and source permissions through retrieval, context assembly, caching and citations. Treat revocation and index freshness as part of the design. [Markdown](https://teqengine.ai/insights/permission-aware-rag/markdown). - [Idempotent AI tool actions and safe retries](https://teqengine.ai/insights/idempotent-ai-tool-actions): Design retries for AI tool actions with operation identity, transactional boundaries and reconciliation. Inspect the actual business effect. [Markdown](https://teqengine.ai/insights/idempotent-ai-tool-actions/markdown). - [AI agent ROI: build a defensible business case](https://teqengine.ai/insights/ai-agent-roi): Assess AI agent ROI through adoption, remaining effort, useful capacity and operating costs. Design a pilot around the assumptions that matter. [Markdown](https://teqengine.ai/insights/ai-agent-roi/markdown). ## Practical tools - [AI ROI calculator: capacity, cost and payback](https://teqengine.ai/insights/ai-roi-calculator): Estimate workflow value, adoption, remaining effort and payback using your own inputs. A private browser worksheet with formulas and explicit limitations. [Markdown](https://teqengine.ai/insights/ai-roi-calculator/markdown). - [AI evaluation sample planner](https://teqengine.ai/insights/ai-evaluation-sample-planner): Calculate a one-sided zero-failure binomial bound and the trials needed for a target. Check the statistical assumptions before using the result. [Markdown](https://teqengine.ai/insights/ai-evaluation-sample-planner/markdown). - [AI vendor evaluation scorecard](https://teqengine.ai/insights/ai-vendor-scorecard): Compare two engineering proposals with fixed criteria, evidence confidence and separate critical requirements. Keep the worksheet private and export it locally. [Markdown](https://teqengine.ai/insights/ai-vendor-scorecard/markdown). ## Engineering examples - [Engineering example: a retry after a lost response](https://teqengine.ai/insights/idempotent-tool-actions-example): Run a transaction example that commits once, loses the response and recovers the result. Inspect duplicate, conflict and tenant-scope behavior. [Markdown](https://teqengine.ai/insights/idempotent-tool-actions-example/markdown). - [Engineering example: permissioned retrieval](https://teqengine.ai/insights/permissioned-retrieval-example): Run a synthetic retrieval example with tenant and document access checks. Inspect permitted context, denied requests and the boundaries still untested. [Markdown](https://teqengine.ai/insights/permissioned-retrieval-example/markdown). - [Engineering example: an evaluation release gate](https://teqengine.ai/insights/evaluation-release-gate-example): Inspect a runnable evaluation release gate that separates average task quality from critical failures, with fixtures you can run yourself. [Markdown](https://teqengine.ai/insights/evaluation-release-gate-example/markdown). ## Optional - [Insights](https://teqengine.ai/insights): Complete writing and case-study index. - [Agent-built codebase checklist](https://teqengine.ai/resources/agent-built-codebase-checklist): Initial inspection questions. - [AI launch readiness checklist](https://teqengine.ai/resources/ai-launch-readiness-checklist): A concise launch review. - [Sitemap](https://teqengine.ai/sitemap.xml): Public canonical URLs. - [Privacy](https://teqengine.ai/privacy): Data and privacy information.