Your reps should be closing deals — not clicking buttons.

Leads arrived from everywhere and got worked in whatever order they landed. Reps lost hours each week to CRM admin and chasing, and nobody could say which enquiries were actually worth a call.

A custom CRM shaped around how they actually sell, lead scoring that ranks every enquiry on fit and intent, and market mapping that finds matching prospects and loads them straight into the pipeline.

Sales doubled. Each rep got roughly nine hours a week back, and that time went straight into selling rather than into the CRM.

Everything that mattered about a patient sat in unstructured clinical documents. Analysts read records by hand, executives waited days for answers, and every sales conversation stalled while somebody went to look something up.

Machine-learning pipelines that read the records and extract structured, evidence-linked intelligence at scale, with every finding traceable to its source document — and a real-time surface built with the sales team so answers arrive while the conversation is still happening.

Two days came out of the sales turnaround. Deal teams walked in with live evidence instead of a promise to follow up, and deal closure doubled inside the first six months on the tool.

Thousands of past enquiries sat cold, written off as dead. And when prospective patients asked AI assistants for the best clinic in their area, the clinic was nowhere to be found.

An AI reactivation engine that works through the entire back-catalogue of old leads and revives them into booked consultations, plus a review-and-content system that captures reviews at the right moments and publishes structured content so the clinic becomes the option AI assistants recommend.

$78k+ pulled back in 90 days from leads they had given up on, and growing visibility as a recommended clinic in AI search. The system runs on its own, and they own it.

On the US–Mexico border, nearly half of enquiries came in Spanish, around the clock. After-hours and Spanish-language enquiries slipped through, and the consultations that did get booked were clogged with people who were never serious — burning the surgeon's time.

A bilingual AI receptionist that answers instantly by voice and chat, day or night, in either language. It runs the whole intake, gathers what a real consultation needs, then hands off to a scoring layer that reads each lead's intent and channel. Only qualified patients land on the calendar.

No enquiry missed, whatever the hour or language. The calendar holds only real candidates, and the clinic can finally see which channels produce patients who actually book.

One of the highest-leverage decisions a pharmacy organisation makes was being made on a fraction of the evidence. Prescriptions, diagnoses and claims sat in three systems that never met, so selection fell back on unit price — the one thing that says nothing about whether a drug works.

Pharmacy, clinical records and insurance claims connected into one view, with an AI analysis layer over it scoring every therapy against the full longitudinal patient picture. The entire stack runs on premise, inside their own infrastructure.

Ten to fifteen percent out of drug spend, taken by choosing therapies that do the most for the patient rather than the cheapest line — each selection arriving with the evidence behind it.

One person can appear across records dozens of times and never look the same twice. Names are spelled differently, addresses go stale, fields disagree — and some of the variation is deliberate. Every unresolved record is a gap with a cost attached.

An AI identity-resolution engine that decides when separate records describe the same person, matching on fields that change over time rather than demanding exact agreement. Resolved identities are screened against sanctions and denied-party lists, running as a full production application with the security posture a critical financial system requires.

Ten percent more identities resolved at thirty percent lower cost than the framework already in place. Live, continuously monitored, serving a range of clients.

Thirty people spent their days keying clinical records into the trial database one character at a time, with a second person checking the first. It capped how many studies the company could take on, because more studies meant more hiring.

An end-to-end pipeline that carries study data across on its own, built on a precision retrieval architecture whose single job is pulling the exact value from the exact document, with an OCR layer for scanned pages. Every field arrives as a character-for-character match, carrying the path back to its source.

100% character-level precision and full traceability — and that exactness is what let the work be handed over whole. All thirty people stayed, now verifying and improving the system instead of typing into it.

Rare neurodegenerative disease is usually found late. The early signal is often already sitting in a report written years earlier and never read again in that light. At population scale nobody can go back through it, so the cohorts stay invisible.

A detection layer over brainwave data, clinical records and claims. Image processing reads the traces, text classification reads the reports around them, and the two combine against claims history to surface patients whose pattern fits the profile before anyone has named it.

A pilot detecting early disease 36% better than the prevailing rate, and previously invisible cohorts that can now be located and sized by hospital and region.

Every case started with a pile of records arriving from a different hospital system in a different shape. All of it had to be read by hand before anyone could plan anything. Days went by before a case was even ready to look at, and the work did not scale past the people doing it.

The full pipeline in production: integration pulls records in from each hospital, classification labels what is inside them, and an AI summarisation layer turns the result into a structured case summary — with the evidence behind every line one click away.

Case summaries land in minutes instead of days, and one pipeline serves a range of hospital clients rather than a single system.