Hire senior AI engineers for fintech and insurance
Engineers who join your team, or a focused pod that owns one build. Either way, AI moves from pilot to production inside your workflows.
FurqanMLOps Engineer
A tkxel company.
Our parent company, tkxel, holds certifications in information security, IT service management, quality management and software development maturity. About tkxel.
AI pilots are easy. Production AI is the hard part.
Four problems keep good AI ideas out of production. Read why hiring a finished AI engineer rarely works.
Unclear use cases
Teams know AI can help but struggle to pick which workflows to automate and what to build first.
Pilots that never reach daily work
A pilot works in a demo. Then it meets real users, messy data, system integrations, approvals and operational constraints.
Too little AI engineering capacity
Internal teams are busy with roadmaps and operations. Few have spare capacity to build agents, copilots or retrieval systems.
Business and technical teams working apart
When the two sides work separately, requirements stay vague and feedback arrives too late to act on.
Two ways to work with us.
Choose the model that fits how your team works.
Forward deployed engineer
Senior AI engineers who join your team and work with the people who own each workflow. They stay through deployment and improvement.
Best when you want AI engineering capacity inside your own team.
AI pod
A senior-led team of AI architects, engineers and specialists that owns one build. From scope to handoff.
Best when you want one AI system built, launched and handed to your team, with the KPI agreed first.
Not sure which fits? That is what the scope call is for.
Built for fintech and insurance workflows.
Where AI can take on the manual work in regulated finance.
KYC and AML checks
Customer onboarding, due diligence, watchlist screening and suspicious activity reports, moved from manual review queues to audit-ready workflows.
Fraud detection
Models trained on your transaction data that flag unusual activity and reduce false positives.
Lending and underwriting
Loan origination, credit decisions and servicing with less manual review and more consistent approvals.
Claims handling
First notice of loss, document processing, routing and settlement, with less manual work at every step.
Underwriting intake
Submission triage, document extraction and risk scoring, so underwriters spend their time on decisions, not data entry.
Claims fraud
Models trained on your claims data that spot unusual patterns and rank referrals for your investigators.
Engineers work within your regulatory requirements throughout, including GDPR, Basel III and Solvency II.
What changes when engineers work inside the workflow.
Faster from problem to production
Discovery, prototype and deployment happen in one team, with no handoff between the people who define the work and the people who build it.
AI your people actually use
Solutions are built with the people who will use them, so they fit how those people already work.
Automation where it saves the most
Engineers see repetitive, manual and decision-heavy work first hand, so automation goes where it removes the most effort.
Pilots that can scale
Solutions are built inside your existing systems, so a pilot that works can extend to other teams.
Tell us what you want to build.
Share the workflow, the systems involved and your timeline. We will recommend embedded engineers or an AI pod and outline the next step.