Services / Data & Cloud
We design the contract first, build APIs that fail predictably, integrate your systems with retries and reconciliation, and document everything twice: once for the developer reading your reference, once for the AI agent parsing your machine-readable spec.
Contracts before code. Machine-readable agent docs. Integrations with evidence.
The business case
Data re-entered by hand, exports nobody scheduled, a partner change that broke a script on a Friday — these are integration problems. A well-contracted API turns them into monitored, versioned connections that scale with the business instead of slowing it down.
Teams re-key data between systems, partners ask for API access, an integration broke silently, or you want AI agents to operate your product. Access to the systems involved and the people who own them is essential.
If the underlying data is unreliable, start with data engineering. If the delivery pipeline around the API is the pain, DevOps & CI/CD is the better entry point. If you need a whole product around the API, product engineering fits.
Where this applies
These are example situations, not pre-packaged promises or claims about past results.
Design and build REST or event-driven APIs with clear contracts, versioning and error semantics, so internal teams and partners integrate without meetings about what a field means.
Learn: can a new consumer integrate from the documentation alone?
Clear the queue of one-off file exports, SFTP drops and manual re-entry by replacing them with documented, monitored integrations.
Learn: how many manual handoffs disappear each week?
Integrate CRMs, ERPs, payment providers and internal services with explicit contracts, retries and reconciliation — not a script that silently fails on Fridays.
Learn: does a partner outage surface in monitoring before customers report it?
Build outbound webhooks with signing, retries, replay and an event catalogue, so consumers build reliable handlers instead of polling.
Learn: can a consumer prove every event was delivered and processed?
Expose your product to AI agents through machine-readable descriptions, deterministic tool schemas and predictable errors, so agents use your API correctly without a human reading the docs.
Learn: can an agent complete the top three tasks from the machine-readable spec alone?
Version, deprecate and migrate an existing API with consumer tracking and dual-running, so nobody's integration breaks on release day.
Learn: do all known consumers work unchanged after each version bump?
How the API works
Human clients and AI agents call the same gateway, where authentication, validation and rate limits enforce the specification before anything reaches your services. The machine-readable spec is checked on every change, so the contract and the code never drift apart.
A versioned specification, structured errors, contract tests in CI, documentation with runnable examples and a sandbox — for human consumers and, where wanted, an agent-ready layer.
Endpoints designed around one eager consumer, undocumented fields that become load-bearing, and integrations with no monitoring and no repair path.
API design & contracts
Good APIs are agreements: what you can call, what you get back, what happens when something fails. We design the contract before writing handlers, so consumers — human or AI — never guess.
Consistent resources, pagination, filtering and structured errors with stable codes, so clients handle failure by design rather than by string-matching messages.
Specifications versioned alongside code, with examples for every endpoint and a diff check so breaking changes are caught in review, not in production.
Explicit versioning policy, deprecation headers and a consumer register, so changes are scheduled rather than sprung on integrators.
Idempotency keys on writes, clear delivery semantics for events and documented reconciliation, so a retry never double-charges a customer.
Integration engineering
Every integration has a failure mode — a partner API goes down, a payload changes shape, a message arrives twice. We design for those failures explicitly instead of discovering them in production.
Connectors to CRMs, ERPs, payments and messaging platforms with authentication, rate-limit handling and retry policies tuned per provider.
Verified, signed webhook endpoints with replay protection, plus outbound webhooks with retries, dead-letter storage and a delivery log consumers can query.
Event-driven integration through queues and streams where ordering, volume or fan-out demands it, with poison-message handling and monitoring.
Scheduled comparison between systems, with a documented repair path, so a dropped message is found and fixed rather than forgotten.
APIs for AI agents
AI agents consume APIs differently: no marketing page, no login portal, no PDF. We design a machine-readable layer alongside the human one — descriptions, schemas and examples that let an agent plan and execute tasks correctly.
Endpoints described in structured, agent-oriented documentation with purposes, constraints and preconditions stated in plain machine-readable text, not prose.
Deterministic, validated tool definitions for MCP and function-calling frameworks, with enums, required fields and examples an agent can follow without trial and error.
Errors an agent can act on: structured codes, hints on whether to retry, re-auth or escalate, and rate-limit signals — so agents recover instead of looping.
OAuth scopes and API keys designed per agent capability, with least privilege and audit trails, so an agent can only do what its grant allows.
Documentation & developer experience
Documentation is part of the deliverable, not an afterthought. We write it for the newest member of the consuming team — and for the agent that has no one to ask.
Every endpoint documented with copy-paste examples in common languages, generated from the specification so it cannot drift from the code.
Step-by-step guides for the three to five things consumers actually do, including authentication, errors and webhooks from day one.
A safe environment with predictable test data, so integrators verify their work before touching production.
A public changelog, breaking-change notices and a defined route for questions, so consumers know where to go when something is unclear.
Tools & frameworks
We work across API styles and the tooling that keeps contracts honest. These are options, not a requirement to use everything on the list.
Framework matched to your language, team and API style — REST, GraphQL or RPC.
Machine-readable contracts versioned with code and checked in CI.
Routing, rate limiting, authentication and usage analytics at the edge.
Queues, events and workflow for integrations that must survive failures.
Tool schemas and agent-oriented documentation for AI consumption.
Least-privilege access for humans, services and agents, with audit trails.
Consumer-driven contracts, load checks and breaking-change detection in CI.
Documentation generated from the spec with live examples and test data.
Tool selection depends on your consumers, languages, existing systems and hosting. We recommend after the assessment, not before it.
Delivery approach
You work with engineers inside the delivery process. Contracts are reviewed before code, and every stage is demonstrated against a real consumer scenario.
Map the systems, existing APIs, integration backlog and consumers, then agree what to build first and what the contracts must guarantee.
Output: Integration assessment, consumer register and prioritised API roadmap.
Model resources, errors, events and authentication; draft the OpenAPI spec and machine-readable agent layer for review before code.
Output: Reviewed API specification, agent tool schemas and integration design.
Implement the first endpoints and the highest-value integration, with contract tests, documentation and a sandbox from the start.
Output: Running API with contract tests, reference docs and sandbox access.
Connect partner systems, add webhooks, queues and reconciliation, and run consumer-driven tests against real scenarios.
Output: Working integrations with monitored delivery and reconciliation evidence.
Transfer ownership with documentation, pairing and operational runbooks; establish the versioning and deprecation policy your team runs.
Output: Runbooks, versioning policy and a team that owns its API surface.
A decision-maker, access to the systems being connected, the people who own those systems, and the consumers — internal, partner or agent — the API must serve.
The contract, versioning policy and integration design are written down and agreed before build. If discovery changes a contract, the change is agreed with you and your consumers first.
Handover & ownership
Everything lives in your repositories from day one. Handover includes the design rationale, the versioning policy and the operational practices, so the API surface keeps its promises after we leave.
Versioned specifications with examples, changelogs and a deprecation policy, so integrations are planned rather than surprised.
Every connection monitored with delivery logs and reconciliation, so missing data is found by a dashboard, not by a customer.
Machine-readable descriptions and validated tool schemas, so AI agents can use your API correctly without hand-holding.
Reference, task guides, sandbox credentials and a support path, tested by having a new consumer integrate without a walkthrough.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing API operations support, or hand over fully to your own team.
Related engineering services
API work often connects to data platforms, cloud delivery or dedicated engineering capacity.
Questions before starting
With the consumers, not the technology. We identify who needs what data and which actions, design the smallest contract that serves them, and build that first. A well-designed API covering real needs beats a comprehensive one covering guesses.
Three things: machine-readable documentation an agent can parse and act on, deterministic tool schemas for frameworks like MCP and function calling, and error semantics an agent can reason about — retry, re-authenticate or escalate. Your human-facing docs and portal stay; the agent layer sits alongside them.
Only with scoped credentials. We design per-agent OAuth scopes or keys limited to specific capabilities, with audit trails and rate limits, so an agent can only do what its grant allows. Exposing an unscoped API to agents would be reckless, and we will say so.
Yes — that is where most integration value hides. We connect legacy systems through APIs, database CDC or file-based bridges as appropriate, and we are honest about where a legacy constraint forces a design compromise.
Depends on the consumers. REST with a solid OpenAPI spec fits most public and partner APIs; GraphQL fits rich, client-driven queries; gRPC fits internal service-to-service performance. We recommend after understanding your consumers, not before.
Contract tests in CI, schema diffing on every change, monitoring on delivery and reconciliation jobs, and a consumer register so we know who to notify before a change. Silence is treated as a bug, not a success.
Following the versioning policy we establish with you: a new version, a deprecation window, headers that tell consumers what is coming, and direct notice to everyone on the consumer register. Breaking changes become scheduled events, not incidents.
Your team owns the code, the specifications, the documentation and the operational runbooks. We pair with your engineers throughout, and we can stay available for later phases or reviews if useful.
Start with the question
Share what needs to talk to what, who consumes it, and whether agents should too. We will agree a practical first contract.