Add AI to the software you already run
We integrate language models into your CRM, ERP, portals, and internal tools, behind your auth and inside your data boundary, without a rebuild or a rip-and-replace.
How it works
The hard part of enterprise AI was never the model. It's the legacy schema nobody documented, the SSO rules, the rate limits, the auditor who wants to know where the prompt went. We do that work: clean interfaces into your existing systems, a model layer you can swap without touching business logic, and observability so you can see cost and latency per feature. Your team keeps its stack and gets AI inside the screens people already use.
A model layer you aren't locked into
We put an abstraction between your application and whichever provider you use, so switching is a config change.
- Works with Anthropic, OpenAI, Azure, Bedrock, or self-hosted models
- Route by task: cheap model for routing, strong model for reasoning
- Automatic failover when a provider degrades
- Per-feature token and cost tracking from day one
Integration into real systems, not sandboxes
We connect to the platforms your business actually runs on, including the ones with no modern API.
- Salesforce, HubSpot, Dynamics, NetSuite, SAP, Workday, ServiceNow
- Legacy databases, SOAP endpoints, flat-file and SFTP feeds
- Event-driven syncs with retries, backfills, and dead-letter handling
- Native handoff into Leadify for sales and marketing workflows
Built to survive production
Every integration ships with the monitoring, security review, and documentation your team needs to own it.
- SOC 2 aligned controls, encryption in transit and at rest
- No customer data used for third-party model training
- Prompt, response, and decision logging for audit
- Runbooks, architecture docs, and handover to your engineers
Where teams use it
Built for real revenue work.
AI inside your CRM
Summarize account history, draft the follow-up, score the pipeline, and surface next steps directly on the record. Reps get the benefit without learning a second tool.
Copilots in your own product
Ship an AI feature your customers see, natural-language search, in-app guidance, automated setup, using your data model and your existing permission rules.
Back-office automation
Connect intake, approvals, and finance systems so requests are read, classified, and routed automatically, with a human approving only the exceptions.
“Two vendors told us we'd need to replatform before any AI could touch our order system. APPIT built a service layer around it in six weeks and left us the repo. Our own engineers shipped the second feature themselves.”
FAQ
Questions, answered.
How do I add AI to my existing software?
You don't rewrite the application. We add a thin service layer that talks to your existing data and APIs, put the model behind it, and expose the result inside the interface your team already uses. Work usually starts with one high-value workflow, an approval, a summary, a lookup, so you get something in production in about five weeks and can judge the payoff before expanding. Everything is built in your repository and deployed to your environment.
What if our systems are old and barely have APIs?
That's the common case, and it's most of the engineering. We work with SOAP services, direct database reads, scheduled file drops, and screen-level automation where nothing else exists, then wrap them in a clean internal API so future work is easy. We also build the sync reliability nobody budgets for: retries, idempotency, backfills, and alerting when a feed goes quiet.
Is my data safe with a custom AI build?
Your data stays in your cloud account or a dedicated environment we manage, and we use enterprise model endpoints where inputs are not retained for training. Access is scoped by the same roles your systems already enforce, and every prompt and response can be logged for audit. Delivery follows SOC 2 aligned controls, and we're glad to work inside your existing DPA, VPC, and pen-test requirements.
Will we be locked into one AI provider?
No, and that's deliberate. Business logic never calls a vendor SDK directly; it calls our model layer, so moving from one provider to another is a configuration change rather than a rewrite. That also lets us route cheap tasks to small models and hard ones to frontier models, which usually cuts inference spend by a third without a quality drop.
Keep exploring
More from the Leadify platform.
Your stack is fine. It just needs AI wired in.
Send us your systems list and we'll come back with an integration plan, a fixed price, and a delivery date.