AI inside the tools your team already has open.
Adoption fails when AI lives in a separate tab. The features that stick are the ones that appear where the work already happens — in the ticket, in the CRM record, in the inbox.
Integration work is mostly unglamorous and mostly decisive: authentication, rate limits, data mapping, retries, and what happens when the provider has an outage. Get that right and the AI feature feels like part of the product. Get it wrong and people stop using it within a fortnight.
We also put cost and quality controls in from the start — token budgets, caching, fallbacks, and an evaluation set — because an AI feature without them tends to produce a surprising invoice and no way to tell whether last week's prompt change helped.
What we build
CRM and sales tooling
Summaries, drafted follow-ups, and enrichment written into the record itself, so a rep sees them without changing what they open.
Helpdesk and support stack
Suggested replies, auto-tagging, and sentiment routing inside your existing ticketing tool rather than beside it.
Inbox and calendar
Triage, extraction, and drafting where the message already is, with sending gated on a human unless you decide otherwise.
Internal tools and data
Natural-language querying over your own data with generated queries shown, not hidden, so results can be checked.
Cost and quality controls
Token budgets, caching, provider fallbacks, and an evaluation set — the difference between an AI feature you can operate and one you can only hope about.
How the engagement runs
- 01
Pick the surface
Where in the existing tool the feature has to appear to get used.
- 02
Wire it properly
Auth, rate limits, retries, and graceful degradation when a provider is down.
- 03
Set the guardrails
Budgets, caching, fallbacks, and an evaluation set before launch.
- 04
Measure adoption
Usage per surface, so a feature nobody touches gets removed rather than accumulating.
This is a good fit if
- Tools with a usable API or extension model
- A specific moment in the workflow where AI would save real time
- Somebody internally who will own adoption after launch
- Willingness to remove features that do not get used
We’d turn this down
- Closed systems with no API and no export path
- "Add AI to our product" without a specific user problem attached
- Integrations built to demo to a board rather than to be used on Monday
Questions we get about this
- Which tools do you integrate with?
- Anything with a reasonable API. Common ones are HubSpot, Salesforce, Zendesk, Intercom, Slack, Google Workspace, Notion, and internal systems over REST. Where a documented API does not exist, we will say so during scoping rather than discovering it mid-build.
- How do we keep AI costs predictable?
- Budgets per feature, caching for repeated queries, smaller models for the work that does not need a large one, and alerting on unusual spend. We model expected cost against your real volume during scoping so the first invoice is not a surprise.
- What if we change AI providers later?
- The integration sits behind an interface so the provider can be swapped without rewriting the feature. Given how fast this space moves, assuming today's provider is permanent is a design mistake.
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