AI is only as useful as the systems it can reach.
A model in a tab is a curiosity. A model that can read your ERP, your CRM, your tickets, your calendar, your billing - bounded by role, audit-logged, and ready to act on what it finds - is a teammate. We build that connection.
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Your existing systems were never built to talk to a language model.
They have APIs (sometimes good ones), permission models (sometimes documented), and idiosyncrasies that nobody outside your team understands. Connecting AI to them properly means understanding your access patterns, modelling them in code, and adding the supervision a model needs.
Most AI-integration projects fail by skipping that work - and end up with a model that can read everything (a security headache) or nothing useful (a productivity wash). We do it the slower way that lasts.
Six things every integration engagement gets.
Integration discovery
Two weeks mapping your systems' API capabilities, your existing permission model, and what each agent actually needs to reach. Output: a written scope before a single line of integration code.
Typed, role-aware tool layer
Each system gets a typed wrapper that the agent calls - typed inputs, typed outputs, permissions enforced at the tool level. The model literally cannot fetch what the calling user does not have access to.
Agent design - read, write, decide
Boundaries documented. Read-only by default; write actions sit behind approval gates. Every operation with consequence stays supervised until trust is earned.
Audit log + reasoning trace
Every action recorded; every decision traceable. Compliance teams get the trail they need; on-call engineers get the data they need when something goes wrong.
Failure handling
Retries, circuit breakers, graceful degradation, alerting before customers notice. Production-grade from day one.
Runbook + handover
Documentation, API-key inventory, escalation paths. The day we hand off, your team owns it. Mainstream technology only - no stack you cannot hire for.
Why these projects usually fail - and how we build them so they don't.
There is a recognisable shape to AI-integration projects that don't survive past the first quarter. Naming the failure mode is the first step to avoiding it.
- Brittle on input variation; one schema change breaks the chain
- Permissions modelled in the prompt, not at the tool - the model can be talked into reaching what it should not
- No reasoning trace when something goes wrong; debugging takes days
- Built once, never updated as the underlying systems evolve
- No clear handover; the integration becomes a black box only the original vendor understands
- Typed tool layer - schema changes throw real errors instead of silent corruption
- Permissions enforced at the tool level - the model literally cannot reach what the user cannot
- Audit log + reasoning trace per action - debuggable when it matters
- Living lessons file per skill - the integration handles edge cases better over time
- Runbook + API-key inventory + escalation paths - your team owns it the day we hand off
The stacks our agents already speak.
ERP - SAP, Microsoft Dynamics, custom regional platforms
Natural-language access, structured queries, write operations gated by role. We have already worked through the auth and schema quirks on multiple custom ERP platforms; the next one will not surprise us.
CRM - Salesforce, HubSpot, Pipedrive, Zoho
Two-way sync, lead scoring, follow-up drafts, pipeline reporting in plain language.
Ticketing - Jira, Linear, Zendesk, Freshdesk, ServiceNow
Triage on arrival, customer history fetched into context, draft response, escalation routed. The agent never sends without approval.
Calendars - Google Workspace, Microsoft 365, Cal.com
Native booking inside chat, availability across teams, smart scheduling that respects working hours and timezones.
Data warehouses - Postgres, BigQuery, Snowflake, ClickHouse
Read-only natural-language queries. Column-level permissions and per-query budgets enforced before the SQL ever runs.
Email + messaging - Microsoft Teams, Slack, Telegram, WhatsApp, Discord, IMAP/SMTP
Agents that read mailboxes, draft replies, post to channels, and pick up tasks from any of the places work actually happens.
How an ERP-integrating team uses it.
19 skills in production, each backed by a typed tool wrapper around the underlying ERP, ticketing, finance, and email systems. The model picks the tool based on the question; the tool enforces the permission - never the prompt. The whole team uses the agent like a teammate across Microsoft Teams and Telegram, reclaiming more than 100 hours a week for higher-stake work.
Three things people always ask.
Will the model see data it shouldn't?
What about hallucinations on numbers and references?
Does this work on-premises, behind our firewall?
Bring us the systems you cannot get talking to each other.
A 30-minute diagnostic call walks through your stack and identifies the 1-2 integrations that would matter most. Free; written summary either way.