ElevateIQ builds and modernizes the systems behind regulated financial and insurance work — from customer journeys and architecture through to AI that is auditable, explainable and safe to put in front of a regulator.
Most engagements start with one and grow into the others — because the hard part is rarely the model, it is the system around it.
We come in where the technology and the business have drifted apart — assess what you run today, find where the cost and risk actually sit, and hand back a roadmap your team can execute. Vendor-neutral, delivery-focused, and written for both the operator and the board.
Map the journey a broker, policyholder or finance team actually takes — including the parts that happen over email, phone and WhatsApp — then redesign it end to end. We deliver working journeys across web, mobile and messaging, not a slide deck of personas.
Data models, integration patterns, service boundaries and the operational concerns that decide whether a system survives year three. We design for auditability and change — versioned rules, traceable data lineage, and clean seams between the parts that must stay deterministic and the parts that can be intelligent.
We build AI into places where being wrong is expensive. The model reads, extracts and drafts; a deterministic engine owns every figure; a human resolves only the exceptions. Everything is versioned, hashed and traceable back to its source — so the output stands up in an audit.
The pattern we apply on every AI engagement in a regulated setting.
Statements, cheque images, chat threads, emails, bordereaux. The model extracts candidate values, cites where each came from, and flags where sources disagree.
Clean, high-confidence values pass automatically. Only conflicts and low-confidence items reach a person — typically a small fraction of the fields.
Figures come from locked configuration and database rows, never the model. Same inputs produce byte-identical output, hashed and archived.
The recurring problems we are called in to solve across insurance and financial services.
Cheques, invoices, statements and chat threads turned into a structured, queryable record with the evidence still attached.
Every entry traced from source to accounting treatment, with a reconciliation difference that visibly closes to zero.
Contracts, slips and letters produced from version-locked rules, with mandatory clauses inserted verbatim and nothing improvised.
Plain-language risk descriptions scored against carrier or product rules, ranked with a transparent explanation of why.
The same engine exposed over web, voice and WhatsApp, so the work happens where the broker or field team already is.
Spreadsheet-run processes and aging platforms replaced incrementally, without a big-bang cutover the business cannot absorb.
Each written as situation, task, action and result — with a mockup of how the delivered system behaves.
A trust facing its first statutory audit, with evidence spread across bank statements, cheque images and WhatsApp threads.
Read the case studyRenewal letters and reinsurer slips generated from messy sources — deterministic, auditable and explainable by design.
Read the case studyCarrier eligibility scored from a spoken or typed business description, delivered over both web and WhatsApp.
Read the case studyA short conversation is usually enough to tell whether this is an architecture problem, a data problem, or a workflow problem — and what it would take to fix.