Data-handling requirements like GLBA, fraud and audit-trail expectations, and SOC 2 or equivalent attestations aren't optional here. A well-scoped tool or diagnostic audit on a platform already in place fits a fast, fixed-scope engagement under Katalyst Launchpad™. Work touching regulated financial or insurance data calls for the fuller validation and architecture of Katalyst Custom™, KitelyTech's fully engineered build process.
Our stakeholder group included board members, clinical leads, compliance officers, and end users — each with different technical literacy and different success criteria. This team navigated that stakeholder landscape as well as any vendor I have seen. They adjusted their communication register depending on the audience without losing the substance. They managed expectations honestly throughout. And they delivered a system that each group can point to as meeting their requirements. That breadth is genuinely uncommon.
The team at KitelyTech finished on time. They delivered a standard of quality and digital presentation that remains unmatched by any technology driven start up in my opinion.
KitelyTech was big enough to cope with my requests but small enough to care.
What made the most difference in practice was the quality of the engineering judgment on this team. Not the ability to execute a specification — that is a baseline expectation. The ability to recognise when a specification was suboptimal, explain why, propose an alternative, and support the client in making a decision about it. That consultative dimension elevated the output beyond what the brief described and resulted in a product that is more fit for purpose than the one we had originally specified.
What stood out was the fact that they really understood what we wanted. They also added their own inputs before executing and delivering everything we wanted. Overall, their ability to take in information and execute and deliver what we needed was impressive.
The final product exceeded all of my expectations
I came into this engagement as a sceptic. We had been through a failed implementation with a previous vendor and I had high standards for what evidence of competence looked like before I would trust a partner with our core systems. This team earned that trust progressively — through the quality of the discovery documentation, the rigour of the technical proposals, the consistency of the sprint deliveries, and ultimately the stability of the production system. I no longer lead with scepticism when recommending them.
Their professionalism and sheer knowledge is reason alone to work with them. This company is definitely on the top of my referral list!
As soon as I talked with one of their team members, their zest and passion became evident. I saw the excellence that their team brought to the table.
I was impressed with their ability to listen and interpret the highly technical nature of our device.
Everything I wanted and more.
A multi-line insurer covering health, life, and property received paper claims in heavy daily volume. Every one needed a person to open it, read it, classify it, and route it.
OCR digitizes each claim on arrival. Natural language processing and machine learning classify it by type, amount, and complexity, extract the key information, and route it into the insurer's existing systems. Claims falling below the confidence threshold route to a person with the extracted data already attached, so the exception path is faster too. Roughly 40% of claims now process automatically.
AI-driven risk report automation
Risk reports arrived in every format a client could produce, and risk engineers read them one at a time. The view of overall exposure was never complete and never current.
A custom NLP platform that extracts, classifies, and categorizes every report by type as it arrives. The pipeline handles native digital documents and scanned ones alike, which is what lets a single classification pass work across sources that share no structure. Engineers work from one complete view of exposure instead of assembling it by hand, and the hours that went into sorting go back into assessment.
AI deal-flow automation
Inbound deal flow grew faster than the team could assess it. Every opportunity needed the same manual first pass against the same criteria. Deal progress lived in inboxes and in whoever was closest to it, so the pipeline was only as current as the last person who remembered to update it.
A custom AI system that runs the first pass automatically, scoring each inbound opportunity against the firm's own written investment criteria and surfacing the ones that merit attention. Deal progression is managed in the system rather than in inboxes, with real-time updates giving the whole team one view of the pipeline. Judgment stays with the investors; what changed is that they spend it on the deals worth judging.
ML and NLP contract management modernization
Contract management at a global insurer was analog: manual handling, slow decisions, limited analytics, and cost, delay, and compliance exposure spread across thousands of documents nobody could analyze as a body.
A machine learning and natural language processing system with a custom interface that took the contract base digital and made it legible. OCR digitizes the paper. ML classifies every contract by risk, policy type, and coverage. NLP extracts the key clauses, terms, and conditions, so compliance and risk questions get answered from the contracts themselves rather than from memory of them.
Machine learning for customer contact timing
The bank's outreach contacted customers at times chosen by the caller's schedule, not the customer's likelihood of answering.
A custom machine learning algorithm that predicts the top three times to contact each client, built with statistical rigor the domain deserves: Kolmogorov-Smirnov and Chi-squared testing, drift management as the customer base changes, architecture that scales with the bank's growth, and monitoring with retraining to hold accuracy over time. Productivity and sales rose measurably.
