AI INTEGRATION
Keep your software. Raise what it can do.
A working system does not need a rewrite to use AI. We put the capability inside the screen your people already have open.
Capabilities we add
- Smart search Search by meaning, not keywords — across documents, tickets and product data via RAG, with the source cited in every result.
- Summarization Structured summaries of long ticket threads, meeting notes, contracts and reports, grounded in the source text to keep invention down.
- Classification and routing Incoming requests sorted by category and priority and sent to the right team — a hybrid of rules and model judgement.
- Data extraction Field-level extraction from invoices, forms, PDFs and email, written straight into the database you already use.
- Process automation Drafting, reply suggestions and checklist verification embedded directly into the workflow.
Who it is for
- Teams happy with their systems but stuck on search and reporting
- Support, legal, procurement and HR functions buried in documents and requests
- Software companies adding AI features to their own SaaS product
- Teams that want a measurable win without a transformation budget
How we work
- 01 Identify Which screen, which task, how many minutes saved. We pick one measurable target.
- 02 Prototype A narrow version running on your real data, with acceptance criteria written up front.
- 03 Integration The capability embedded in your interface and APIs, with permissions and logging.
- 04 Production Load testing, cost ceilings, monitoring and rollout.
- 05 Evolution Improvements driven by measurement, then the next capability.
Frequently asked
Is our data sent to a model provider?
That is your call. For constrained organizations we build the integration on an on-premises AI server system and the data stays inside. Without that constraint, API models are also an option.
How do you manage the risk of wrong answers?
Output is grounded in sources and users see the citation. Before launch we build an evaluation set from real examples and agree the acceptance threshold with you.
Is the cost predictable?
We measure cost per use during the prototype and set ceilings and alerts. At high volume, running a local model is usually the more predictable option.
Our system is old and has no API.
We write a thin access layer first. If the scope is larger, software modernization becomes its own conversation.
Start small. Finish in production.
No deck needed. 20 minutes. The rest is up to you.