AI Strategy, Architecture & Development

Project assessment: establish the engineering boundary

We start with the business process, not the choice of AI tool. Who will use the application? What data will it hold? What happens if a transaction fails, a customer sees another tenant's records, or the service is unavailable? For an existing prototype, we examine the code, dependencies, data model and deployment configuration alongside the intended scope.

• Review functional requirements, integrations, user roles and operational constraints.
• Assess data sensitivity, customer commitments, security requirements and the consequences of failure.
• Identify prototype shortcuts, architectural gaps and dependencies that may prevent production use.
• Classify each workstream: suitable for supervised AI generation, requiring engineer-led implementation, or unsuitable for the selected platform.

You receive a prioritized risk register, a component-level delivery recommendation and an implementation roadmap with assumptions and dependencies. A bounded internal tool may need a few targeted changes; a customer-facing platform may need a different architecture.

Architecture consulting and enforceable guardrails

Our engineers define how the application fits together: component responsibilities, API contracts, data ownership, trust boundaries and deployment topology. We record the decisions that generated changes must respect and identify where permissions and automated checks must enforce them.

• Separate tenant context, identity and authorization from presentation-layer behavior.
• Define approved integration patterns, transaction boundaries, retry behavior and failure recovery.
• Set repository rules, dependency restrictions, required reviews and test expectations.
• Design access controls, secrets handling, audit logging, backup and restoration procedures.

Architecture instructions help an agent follow a design; they cannot enforce security on their own. We connect the guidance to executable controls through workspace configuration and quality and security gates.

Engineer-led development where vibe coding is not appropriate

Some work cannot be accepted on the strength of a generated preview. When the consequences or platform limitations rule out a prompt-led approach, our engineers design, implement and verify the component under an agreed technical specification.

• Multi-tenancy: tenant-aware authorization and data access, isolation across files, caches, search and background jobs, and tests that attempt cross-tenant access.
• Security-sensitive workflows: server-side permissions, privileged operations, identity-provider integration, session handling and auditable administrative actions.
• Encryption and key management: established cryptographic libraries and managed key services, encryption in transit and at rest, access separation, key rotation and recovery procedures. We do not invent cryptographic algorithms.
• Business-critical processing: payment and order workflows, idempotency, reconciliation, transactional consistency and recovery from partial failures.
• Enterprise integrations: controlled access to ERP, CRM and legacy systems, explicit data contracts and failure handling that protects existing operations.

These are areas for accountable engineering, not a blanket ban on every AI-assisted coding tool. Engineers may use approved assistants for bounded tasks, but own the design, implementation and independent verification. If a builder cannot support a mandatory isolation, hosting or audit requirement, we develop the affected subsystem—or the application—outside that builder.

Integration into one working application

A secure subsystem is not useful if the rest of the application can bypass it. We integrate engineer-built services with the generated interface and approved application components through versioned APIs, consistent identity and authorization, and explicit error contracts.

Our delivery scope can include data migration, integration and end-to-end tests, tenant-isolation checks, deployment pipelines, monitoring and rollback procedures. We verify the critical paths across the whole application and document ownership so your team can maintain it after handover.

Policy people will actually read

A useful AI usage policy fits on a few pages and answers operational questions. Which classes of repository may use external models? May customer data, secrets or unreleased source appear in a prompt? Which tools and plans are approved, and who grants exceptions? We draft this with your security, legal and engineering leads, and keep the language concrete enough to audit.

We are not your counsel. Questions of intellectual property, licensing exposure and regulatory duty go to your legal team, and we prepare the technical facts they need.

Prompt playbooks tied to real work

Generic prompt tips age quickly. A playbook is built from your stack: how to ask for a migration in your ORM, how to request a test that exercises the failure path, how to have an assistant explain a legacy module before changing it. Each entry pairs a prompt pattern with the review step it requires.

• Task recipes for refactors, test generation, incident forensics and documentation.
• Anti-patterns with examples from your own pull requests, such as accepting a plausible but nonexistent library function.
• Context-hygiene guidance: what to include, what to strip, and when to start a fresh session.

GitHub Copilot training and supervised review

The skill that matters most is not prompting; it is reading code you did not write at speed and spotting what is subtly wrong. Workshops use your repositories and run as paired exercises: find the invented API, the swallowed exception, the test that cannot fail.

GitHub Copilot training covers the editor features your plan includes, repository-level custom instructions in .github/copilot-instructions.md, and the limits of those instructions. Instruction files steer suggestions. They do not enforce anything, which is why training sits next to the controls described under automated guardrails.

Security guardrails, stated honestly

We document the threats worth planning for: prompt injection through issue text or dependencies, over-permissioned agents, secrets pasted into chat, and unreviewed dependency additions. For each we separate the written rule from the technical control that backs it, such as least-privilege tokens, network egress limits, and required review on lockfile changes.

Deliverables

• Scope assessment, risk register and a component-level AI-versus-engineer delivery plan
• Target architecture, interface contracts and documented security decisions
• Implemented guardrails, review criteria and verification evidence for the agreed scope
• Engineer-built components integrated into the application, with tests and deployment documentation
• Operational handover, ownership and maintenance guidance
• Where included: AI usage policy, repository prompt playbooks and role-based workshops

Engage us from assessment through delivery

Start with a scoped assessment, retain our architects to guide your team, or commission implementation and integration. We agree the deliverables, acceptance criteria and responsibilities before development begins. You do not have to replace a working prototype to address a specific risk; equally, we will not recommend keeping a platform that cannot meet a mandatory requirement.

Where to go next

Policy needs an environment to live in; see agentic workspace and platform engineering. The wider picture is on the AI-accelerated engineering overview. Ready to talk through your situation? Contact our team.