Common Questions. Honest Answers.

Tech Delivery Partners (TDP) provides two core services: Responsible Custom AI Agent Engineering and AI Agent Governance. We build custom AI agent systems for enterprise organizations in regulated industries, and we build the governance frameworks that allow those organizations to adopt AI safely and demonstrate accountability to regulators and stakeholders.

We specialize in regulated industries where AI governance is most demanding: healthcare, financial services, insurance, legal, government and public sector, manufacturing, education, and enterprise SaaS. These are environments where AI errors carry real consequences and where regulatory requirements are non-negotiable.

For TDP, responsible AI means every system we build includes human oversight for consequential decisions, defined safety boundaries for what the AI is and is not allowed to do, complete documentation of how the system works, adversarial testing before deployment, and governance structures that remain accountable over time. Responsibility is an engineering requirement, not a marketing claim.

We are a delivery partner. We write code, build systems, and create governance frameworks. We are not a software product company and we are not a strategy consulting firm that produces slide decks. We produce working AI systems and documented governance frameworks, things you can use, audit, and build on.

Three things distinguish TDP. First, we specialize in regulated industries, we understand compliance environments that most AI firms do not. Second, safety and governance are engineering requirements in every project, not optional add-ons. Third, we stay engaged after delivery, monitoring, maintaining, and evolving your AI program rather than handing over a deliverable and disappearing.

A chatbot responds to questions. An AI agent takes actions. AI agents can retrieve information from multiple systems, make decisions based on that information, execute tasks (draft documents, file forms, update records), and route work to the right people. The agents we build interact with your actual business systems and perform real work, not just answer questions.

We are technology-agnostic and select the right tools for each project's specific requirements. We work with foundation models from Anthropic, OpenAI, Google, and others. For orchestration, we have experience with frameworks including LangChain, LangGraph, CrewAI, AutoGen, n8n, and MCP (Model Context Protocol). Model selection is driven by performance, security requirements, and governance needs, not vendor preference.

Prevention happens at multiple levels. At the design level, we define explicit boundaries for what the agent can and cannot do. At the engineering level, we implement output validation, confidence thresholds, and prompt injection protection. At the workflow level, we design human approval gates for high-stakes actions. At the testing level, every agent undergoes red team testing before deployment. And at the operational level, we monitor output quality continuously after go-live.

That is not our design goal, and it is usually not the right business outcome either. The AI agents we build handle high-volume, repetitive, lower-judgment work so that your team can focus on the higher-value decisions and relationships that require human expertise. Our human-in-the-loop design philosophy keeps your people in control of consequential decisions. The goal is a more capable team, not a smaller one.

Yes. Enterprise system integration is a core component of our engineering service. We build integrations with EHR systems, CRM platforms, ERP systems, document management systems, ticketing platforms, and custom internal APIs. We assess your existing infrastructure during discovery and design the integration architecture before we write any agent code.

Red team testing means deliberately attempting to make the agent behave in undesirable ways, feeding it adversarial inputs, edge cases, attempts to manipulate its behavior through prompt injection, unusual data, and scenarios designed to trigger errors. We test systematically against the safety boundaries defined in the requirements phase. Anything the red team breaks gets fixed before the agent goes anywhere near production data.

Timelines vary significantly by project scope and complexity. A focused single-workflow agent can be delivered in 10 to 14 weeks from kickoff to go-live. A multi-workflow system with complex integrations and extensive compliance requirements typically takes 18 to 24 weeks. We provide a project timeline estimate during the requirements phase after we understand your specific situation.

Yes. Everything we build for you, code, documentation, training data, integrations, governance frameworks, is your intellectual property. You are not locked into TDP for ongoing operation. We want you to stay with us because the relationship delivers value, not because we have locked you in technically.

We offer ongoing monitoring and maintenance retainers that include performance monitoring, drift detection, regular accuracy evaluations, incident response support, and optimization recommendations. AI systems change over time, model updates, changing data distributions, evolving business requirements. Ongoing monitoring catches drift before it becomes a problem.

Yes. We offer an AI system audit service for existing deployments. We evaluate accuracy, safety controls, governance documentation, integration architecture, and operational monitoring. We produce a structured assessment with specific remediation recommendations. This is often the right starting point for organizations that deployed AI quickly and are now concerned about what they may have missed.

AI governance is the set of policies, processes, roles, and controls that determine how AI is adopted, operated, and monitored in your organization. Without it, AI deployment creates legal exposure, reputational risk, compliance gaps, and accountability gaps when something goes wrong. In regulated industries, governance is increasingly a legal or regulatory expectation, not just best practice.

It is never too late, and this is actually a common entry point for governance work. We conduct an AI readiness assessment of your existing deployments, identify governance gaps, assess regulatory exposure, and build the governance framework around your current state. Retrofitting governance is more work than designing it from the start, but it is far better than having no governance when regulators or auditors ask questions.

We align governance frameworks to NIST AI Risk Management Framework (AI RMF), ISO 42001 (AI Management System standard), EU AI Act requirements, industry-specific regulations (HIPAA, FINRA, NAIC model laws, FERPA), and emerging state-level AI regulations. We do not force a single framework on every client, we design governance that satisfies your specific regulatory obligations.

A complete governance framework includes: written AI use policies, defined accountability structures (who is responsible for what), AI procurement review processes, risk assessment procedures, documentation standards (decision logs, model cards), access control design, incident response plans, ongoing monitoring requirements, and training programs for staff at different levels. The framework is practical documentation your team actually uses, not a theoretical document that sits on a shelf.

Yes. The source of the AI tool does not change your organization's accountability for its outputs. If an AI tool makes a decision that affects a patient, a customer, or a regulated process, your organization is accountable for that decision, regardless of who built the underlying model. Governance applies to how you use AI, not just to how it was built.

We design governance frameworks to be durable rather than brittle. The core principles, accountability, documentation, transparency, human oversight, incident response, are consistent across regulatory frameworks even as specific requirements evolve. We also include a regulatory monitoring commitment in our ongoing support retainers, which means we track relevant regulatory developments and update your framework when new requirements apply.

Our AI readiness assessment evaluates your organization's current state across five dimensions: data quality and availability, security posture, compliance obligations, existing AI deployments and their governance status, and organizational readiness (people, training, accountability structures). The output is a detailed gap analysis and a prioritized roadmap for responsible AI adoption. This is often the right starting point for organizations exploring AI adoption for the first time.

Yes. Board-level AI risk communication is a specific deliverable we provide. We help you articulate what AI your organization uses, what risks it creates, how those risks are managed, and what your governance structure looks like, in plain language that a board of directors can evaluate and provide oversight on. This is increasingly important as boards face questions about AI from investors, regulators, and institutional clients.

Yes. We provide governance training at two levels. Leadership and board training focuses on AI risk, fiduciary oversight responsibilities, and how to evaluate AI governance programs. Operational training helps the teams who work with AI systems understand their specific governance responsibilities, how to use the documentation and review procedures we put in place, and how to escalate concerns appropriately.

Building governance before deployment is the ideal starting point. We conduct a readiness assessment, build your governance framework, and establish the policies and review processes that will govern AI adoption before any AI is deployed. Organizations that do this work upfront avoid the much more expensive process of retrofitting governance onto systems that were built without it.

The starting point is a 30-minute discovery call. No preparation is needed, just come ready to describe the problem you are trying to solve and the constraints you are working within. We will ask questions, give you an honest initial reaction, and tell you what a next step could look like. There is no cost for the discovery call and no obligation to continue.

Discovery is typically a two to four week structured engagement before any design or engineering begins. We interview key stakeholders, map your existing processes, assess your data and systems, understand your compliance environment, and document the problem we are trying to solve. Discovery produces a written problem statement, stakeholder map, regulatory constraint inventory, and data availability assessment. No engineering begins until discovery is complete and signed off.

Our process requires meaningful client engagement at key points, discovery interviews, requirements sign-off, architecture review, sprint demonstrations, and user acceptance testing. We do not need your team in meetings every day, but we do need access to the people who know the business processes, the subject matter experts who will validate the AI's outputs, and the decision-makers who can sign off on design choices. Engagements where clients disengage tend to produce worse outcomes.

Requirements changes are a normal part of complex projects. We have a defined change management process, changes are documented, their scope impact is assessed, and the client approves any schedule or cost adjustments before we proceed. We do not absorb scope changes silently or build undocumented features. If a change is significant, we pause and have an honest conversation about what it means for the timeline and budget.

Knowledge transfer is built into the project, not an afterthought at the end. Documentation is produced throughout. Training for your operational and technical staff is a dedicated phase. By go-live, your team has the documentation, training, and access they need to operate the system. If your team needs additional capability development, we offer ongoing technical advisory retainers.

Yes. We work closely with internal IT, security, and development teams throughout every engagement. Security review, infrastructure decisions, and integration design all require collaboration with your internal technical staff. We have experience working within enterprise change management processes and enterprise security review requirements.

Every engagement includes a post-deployment warranty period with defined support obligations. If the agent does not meet the acceptance criteria defined during the requirements phase, we address the gap. Beyond the warranty period, our ongoing monitoring retainers provide the infrastructure to detect performance issues early and address them before they become significant problems.

For organizations that want to validate the approach before committing to a full engagement, we offer scoped pilot projects focused on a single workflow. A pilot gives you working technology, a real assessment of performance, and the information you need to decide whether to expand the program. Pilots are structured as complete engagements with their own requirements, testing, and delivery phases, not quick prototypes.

We work within your security requirements and data handling policies. For healthcare clients, we operate under HIPAA BAA arrangements. For financial clients, we work within data classification and handling standards. Where possible, we use anonymized or synthetic data for development and testing, using production data only when necessary and always within defined security controls. Data handling is addressed explicitly in our engagement agreements.

Yes. Deployment architecture is one of the first decisions we make together during discovery. Options include cloud deployment (AWS, Azure, GCP), on-premise deployment on your own infrastructure, hybrid architectures, and private cloud environments. For clients with strict data residency or security requirements, we design the architecture accordingly, including air-gapped deployments for sensitive use cases.

Your data does not leave your control for model training purposes. The AI agents we build use foundation models (like those from Anthropic or OpenAI) through API calls, your data is processed, not retained by those providers for training when you use enterprise API arrangements. We help you configure your AI systems to meet the data retention policies of your chosen model providers.

Prompt injection protection is an engineering requirement in every agent we build. We implement input sanitization, separate instruction and data contexts, use system-level guardrails that cannot be overridden by user inputs, and red-team test specifically for injection vulnerabilities before deployment. We also design agents with the principle of least privilege, they only have access to what they need for their specific task.

Yes. NDAs are standard at engagement initiation. We also execute data processing agreements (DPAs) for engagements involving regulated data, and HIPAA Business Associate Agreements for healthcare clients. We understand that enterprise procurement requires these agreements and we move quickly on legal documentation so it does not slow down project initiation.

Projects are typically priced as fixed-scope engagements with defined deliverables and acceptance criteria. We do not price by the hour for project work, fixed pricing gives you budget certainty and aligns our incentives with delivering results rather than extending engagements. Ongoing monitoring and maintenance retainers are priced monthly. We provide a detailed project estimate after the requirements phase when the scope is fully understood.

Project investment varies significantly by scope, complexity, and the regulatory environment. A focused pilot project with a single workflow may be appropriate for organizations that want to validate the approach before committing to a full program. Multi-workflow enterprise systems with complex integrations and extensive compliance requirements are larger investments. We do not publish price ranges because they are almost always misleading without scope context, the discovery call is where we can give you an honest estimate.

Yes. The enterprise AI readiness assessment is available as a standalone engagement. It gives you a complete picture of your governance gaps and a prioritized remediation roadmap without committing to a full framework implementation. Some clients use the assessment to build the business case for governance investment internally before engaging us for implementation.

Yes. We offer scoped engagements for specific phases, a discovery and requirements engagement, an architecture review, a red team assessment of an existing system, or a governance framework design without implementation. Organizations with strong internal technical teams often engage us for advisory phases while handling implementation internally.

Our primary client base is US-based organizations, though we work with international organizations that operate in US-regulated markets or who need governance frameworks that address both US and international regulatory requirements including GDPR and the EU AI Act. Our team works across time zones and we have experience with distributed project delivery.

Yes. We design AI systems for healthcare clients with HIPAA compliance requirements addressed in the architecture from the start. We execute BAA agreements, design data handling architectures that meet PHI protections, implement audit logging for AI access to health data, and produce the documentation that demonstrates HIPAA compliance for AI systems. We are not healthcare attorneys, but we work in close coordination with your compliance and legal teams.

Yes. We have experience with financial services regulatory requirements for AI including FINRA guidance on algorithmic systems, SEC considerations for AI in investment advisory, OCC model risk management requirements (SR 11-7), and BSA/AML obligations for AI-powered transaction monitoring. We design AI systems for financial clients that satisfy examiner expectations for model documentation, explainability, and governance.

Government contractors face both agency-specific AI requirements and the general federal AI governance landscape (Executive Orders, OMB guidance, NIST AI RMF). We design governance frameworks for contractors that address their specific agency obligations and help them demonstrate responsible AI practices in proposal submissions, contract performance, and audit situations.

Possibly. The industries we list are where we have the deepest domain experience. For industries not listed, the right answer depends on the specific use case, your regulatory environment, and whether we have the domain knowledge to design and evaluate the AI system responsibly. We will give you an honest assessment in a discovery call, if we are not the right fit, we will tell you and try to point you in the right direction.

MCP (Model Context Protocol) is an open standard developed by Anthropic that provides a consistent way to connect AI models to data sources and tools. We work with MCP for applicable integration scenarios, it standardizes how agents access external context, which improves security and maintainability. MCP is one of several integration approaches we evaluate for each project's specific requirements.

Yes. Document processing, PDFs, Word documents, scanned forms, emails, clinical notes, is one of the most common use cases for enterprise AI agents. We build document ingestion pipelines that handle format conversion, optical character recognition for scanned documents, extraction of structured data from unstructured text, and quality validation of extracted content.

We are model-agnostic and select based on the requirements of each project. Key selection criteria include task performance on your specific use case, security and data handling capabilities, deployment options (cloud vs. on-premise), cost efficiency at your expected usage volume, and governance features like audit logging and content filtering. We evaluate multiple models during the architecture phase and make selection recommendations with clear rationale.