Service · Integration
Extension & integration support
AI is only useful where the work already happens. We put it inside the editor, the browser, the chat client, and the systems of record — and we connect it to your data with auth, permissions, and rate limits designed in from day one, not bolted on after the security review.
The copy-paste tax
Every context switch is a place adoption dies.
A model that can't see your systems makes the human the integration layer. Someone opens a tab, copies a ticket in, copies the answer out, reformats it, and pastes it somewhere else. That's not automation — it's a slower version of the original job with an extra step.
Context that has to be re-supplied
Every session starts from zero. The model doesn't know your codebase, your customers, your ticket history, or last quarter's decisions — so a human types it in again, imperfectly, every time.
Output that goes nowhere
The answer is good, and now it needs to become a Jira ticket, a CRM note, a pull request, or a row in a sheet. Manually. Which is where the time savings quietly evaporate.
Shadow workarounds
When the sanctioned path is slow, people paste customer data into personal accounts to get their job done. You don't fix that with a policy email — you fix it by making the sanctioned path the fastest one.
The connective tissue
MCP servers: your systems, as tools
The Model Context Protocol is an open standard for exposing data and actions to AI agents. Build one MCP server for a system and any MCP-compatible client can use it — rather than writing a bespoke integration for every AI tool your organisation adopts and re-writing them all next year.
In practice, an MCP server is a small service that says: here are the things you may look at, and here are the actions you may take. That surface is where the real design work lives.
What we build into every server
- A deliberate tool surface. Not "here is our whole API". A focused set of well-named, well-described operations — because a bloated, ambiguous tool list makes an agent worse, not more capable.
- Read/write separation. Read tools are safe and parallel-friendly. Anything that changes state is separate, explicitly named, and gateable.
- Real permission enforcement. The server checks what this user may see, server-side. Not a prompt asking the model to be careful.
- High-signal responses. Returning a 400 KB JSON blob wastes context and buries the answer. Responses are shaped for a reader, with pagination and filtering.
- Approval gates. Irreversible actions — sending, deleting, posting, paying — stop and ask a human, with the exact action shown.
- Audit logging. Who asked, what ran, what came back. The first thing anyone wants after an incident, and the first thing that's missing.
// Read — safe, parallel, no gate search_accounts(query, limit) get_account(id) // scoped to caller list_open_opportunities(owner) get_interaction_history(account_id, since) // Write — gated, audited, explicit create_note(account_id, body) update_opportunity_stage(id, stage) ↳ requires human approval ↳ logged with actor + before/after // Deliberately NOT exposed // delete_*, bulk_export, billing_*, // anything touching PII beyond the // caller's own territory
The tool surface is a security boundary. What you choose not to expose matters as much as what you do. We design that list with your security team present, not as an afterthought in week five.
Surfaces
Where we put the AI
Editor & IDE extensions
VS Code and JetBrains extensions that carry your organisation's context: internal conventions, architecture decisions, shared skills, and access to your private services. Configuration and standards distributed centrally, so every engineer gets the same setup instead of twelve personal configs.
Includes: extension scaffolding and packaging, settings sync, private registry publishing, org-wide policy, telemetry that respects privacy, and rollout support.
Browser extensions
Chrome, Edge, and Firefox extensions that add AI where a web tool has none — summarising the page, extracting structured data, drafting in place, or acting as a bridge to an internal system a SaaS vendor won't integrate with. Deployed privately to your organisation, not the public store, when that's the right call.
Includes: Manifest V3 build, permission minimisation, enterprise policy deployment, secure token handling, update channel.
Slack & Microsoft Teams
The surface with the highest adoption per dollar spent, because it requires nobody to learn a new tool. Bots that answer from your knowledge base with citations, triage incoming requests, summarise long threads, and file tickets — with channel-scoped permissions and clear escalation to humans.
Includes: app manifest and OAuth, slash commands, interactive approvals, thread context handling, per-channel access rules.
Business system connectors
Jira, Linear, Salesforce, HubSpot, Zendesk, ServiceNow, Notion, Confluence, GitHub, GitLab, Google Workspace, Microsoft 365, Airtable, Snowflake, Postgres — and the internal API nobody outside your company has heard of, which is usually the important one.
Includes: OAuth or service-account setup, field mapping, sync strategy, webhook handling, rate-limit and retry logic, sandbox testing.
Email & document workflows
Shared inbox triage that routes and drafts. Attachment extraction into structured records. Document generation into your real templates — Word, Excel, PowerPoint, PDF — with your branding and formatting intact, not a plausible-looking approximation of it.
Includes: inbox connectors, classification rules, template binding, review queue, delivery back into your systems.
Automation platform glue
You may already own Zapier, Make, Power Automate, or n8n. Often the fastest route is a small, well-built custom step inside a flow you already run — rather than a new platform and a new thing to maintain. We'll say when that's true.
Includes: custom actions and webhooks, error handling and dead-letter queues, idempotency, cost guardrails.
Where these projects actually fail
The hard parts, handled up front
Integrations rarely fail on the interesting part. They fail on the six things below — which is exactly why we scope them in week one instead of discovering them in week six.
Identity & permissions
Does the AI act as the user or as a service account? If it acts as itself, it can surface documents the user was never allowed to see. We resolve this before any code, because retrofitting it means rebuilding retrieval.
Credential custody
Secrets never live in prompts, client code, or conversation history — all three are persisted and readable later. Tokens sit in a secret store, injected at the boundary, scoped as narrowly as the vendor allows, and rotatable without a redeploy.
Rate limits & quotas
Third-party APIs throttle, and AI workloads are bursty in ways human usage isn't. Backoff, queueing, batching, and caching designed in — plus circuit breakers so one wedged integration can't take the rest down with it.
Prompt injection
Once an agent reads external content — a web page, an inbound email, a ticket from a customer — that content can contain instructions. Untrusted input is treated as data, tool surfaces are minimised, and anything consequential passes a human gate.
Failure behaviour
What happens when the vendor API is down, the token expired, or the model returns something unparseable? Retries, graceful degradation, dead-letter queues, and alerts that reach a human rather than a log file nobody reads.
Change management
The best integration nobody knows about is worth nothing. Launch comms, a short guide, an internal champion, and a feedback path — plus usage telemetry so you can tell real adoption from a launch-week spike.
What you get
Every integration engagement delivers
- Source code in your repository, under your licence
- A documented tool surface — what's exposed, what deliberately isn't, and why
- Auth and secret-handling setup, with a rotation procedure
- Automated tests, including against vendor sandboxes where they exist
- Deployment pipeline and environment configuration
- Monitoring, audit logs, and alerting
- A runbook: how to operate it, and what to do when it breaks
- A rollout plan and user-facing guide
Common questions
Our security team will need to review this.
Good — bring them to the scoping call. Identity model, data flow, secret custody, and the tool surface are architecture decisions, not paperwork, and they're cheap to get right at the start and expensive to change later. We produce a data-flow diagram and a written threat summary as standard deliverables.
Can it work with our internal system? It's not a well-known product.
Usually yes. If it has an API, a database, or even a scheduled export, there's a path. Genuinely closed legacy systems sometimes need a read-only replica or a file-drop bridge instead — less elegant, but it works and it's honest about the constraint.
We already pay for an AI feature inside the tool. Why build anything?
Often you shouldn't. Vendor-native AI is improving fast and it's already integrated. We'll tell you when the built-in feature covers it. Custom work earns its place when you need to cross systems the vendor doesn't reach, encode your own procedures, or control cost and data handling in ways a SaaS toggle won't allow.
What about MCP specifically — is it a safe bet?
It's an open standard with broad and growing client support, which makes it the most portable option currently available. And the underlying work — a clean, permission-checked tool surface over your systems — is valuable regardless of protocol. If the wire format ever changes, that layer survives it.
Who maintains it afterwards?
You can. It's ordinary code with tests, docs, and a runbook, and we run a handover session with whoever will own it. Many clients keep a light retainer for vendor API changes and upgrades — but that's a choice, not a dependency we engineer in.
Where does the copy-pasting happen?
Tell us which two systems your team is manually shuttling information between. That's almost always the first integration worth building.