We help teams define, build, and scale products — pairing user-centered design and strong engineering with AI-driven capabilities, modular architectures, and an Innovation Lab that compounds speed.
Trusted by leading enterprises and healthcare teams
Modern product teams juggle discovery, AI, modernization, and scale all at once. These are the challenges we're built to solve — with the right tools, agility, and engineering depth.
Embed AI into real user workflows, not bolt it on.
Modernize legacy product platforms without disruption.
Turn product data into actionable insights.
Design API-first, modular product architectures.
Define & validate product goals before committing to solutions.
Release features through rapid, reliable iterations.
Build for cloud-native scalability from day one.
Learn from real usage to expand product intelligence.
Everything needed to take a product from idea to scale — design, intelligence, data, and cloud-native engineering, grounded in deep domain expertise.
Discovery, UX research, and product design that ground every decision in real user problems, goals, and data signals.
Embed AI and ML into real workflows — from predictive scoring to generative capabilities — so intelligence drives the product, not the demo.
Turn product data into actionable insights — clean data foundations, analytics, and feedback loops that expand product intelligence.
Architect for elastic scale from day one — resilient, observable, cloud-native systems that grow with workload, not headcount.
Modular, API-first architectures and strong engineering practices that enable rapid iteration and clean integration.
Deep experience across Healthcare, BFSI, and Life Sciences — so engineering decisions are informed by the realities of your industry.
A structured path that connects product strategy to engineering, business value, and a forward roadmap — built with speed, clarity, and AI at the core.
Work closely with product and business stakeholders to understand the core problem, target users, and success criteria before development begins.
Define the system architecture, technology stack, data flows, and AI readiness to ensure scalability, security, and long-term product evolution.
Build product features iteratively, embedding AI where it delivers real value — automation, intelligence, personalization, and actionable insights.
Conduct User Acceptance Testing and deliver a usable MVP within the first few months, focused on essential features and early user feedback.
Support long-term growth through monitoring, performance optimization, structured releases, and continuous improvement driven by real usage.
Product engineering ran for two decades on a clean handoff. Requirements went in one end, releases came out the other, and the customer appeared at UAT. That worked while the unknowns lived in the interface.
Once AI moved into the product, the unknowns moved into the customer's data. Their document variants. Their exception paths. The field that three departments populate differently. The claim that fails on a payer rule nobody documented. None of that is recoverable from a discovery call — it is only recoverable from a deployment.
Forward Deployed Engineer
A production engineer who works inside the customer's environment, sees what actually breaks against real data, and merges the fix into your product — rather than writing it up and filing it behind a quarter of backlog.
Where requirements come from
When the spec is written
Who holds the context
Feedback latency
What happens to edge cases
The definition of done
A working prototype now takes days. The distance between that prototype and something a regulated customer will run unsupervised is where programmes stall — and it is almost entirely made of their data, not your code.
Engineering capacity is no longer the constraint on AI features. Knowing which of the customer's fourteen exception paths actually matters is. That knowledge does not survive a handoff document.
Customer-specific patches quietly become customer-specific branches, and the cost lands on the platform team eighteen months later. Forward deployment only works with a standing rule for what goes back into core.
How our forward deployed engineers are staffed, what they own inside your release train, and how we stop customer work from forking your product.
Real discovery workshops with our teams and clients across the US, South East Asia, and India — where product goals get defined before a line of code is written.
We invest 10% of our team in Innovation Labs to stay ahead of market trends, accelerate delivery, and proactively build reusable solutions — so clients start with roughly 30% of the work already done.
Frameworks for knowledge bases, fraud detection, predictive scoring, and medical coding (CAC).
Migration accelerators for PowerBuilder, MS Access, Oracle Forms, and mainframe apps.
Standardized data lakes & marts with built-in connectors for EHRs, CRMs, and core banking.
Templates for GCP, AWS, and Azure with built-in cost optimization modules.
Internal scaffolding to generate API templates with observability built in.
AI acceleration delivered on programs referenced by IBM.
An AI product purpose-built for construction development workflows.
A platform customized for a Bay Area startup.
Productive performance in a short span of time comes from clear ownership. Every engagement runs on a governance model with named accountability across people, process, and technology.
A rapid-response team with elite qualifications that resolves critical issues fast, minimizing downtime and risk.
Real-time collaboration and adaptive planning that keep responses swift as project dynamics change.
Continuous review of objectives and outcomes to keep work aligned with strategic goals and informed decisions.
Continuous risk assessment and mitigation to identify and address threats proactively.
Transparent communication with sponsors and executives through bi-weekly, monthly, and quarterly reviews.
A seamless integration framework that adopts new technologies with minimal disruption.
Every engagement follows the same problem → solution → measurable impact arc. Here are three shipped products across healthcare and fintech.
Common questions about product engineering, AI integration, MVP timelines, scaling, and our Innovation Lab.
Explore our latest engineering insights, architectural blueprints, and practical playbooks.
India's DPDP Act and the EU AI Act both tighten enforcement in 2026. Here is why most GCC AI programs still run on a data governance model built for outsourcing, not AI, and what closes the gap.
Agentic systems make five to thirty times more model calls per task than a standard chatbot, yet most finance teams still model AI spend as a flat subscription line. That mismatch is now the most common reason AI agent programs get frozen mid year.
Veracode's 2026 GenAI Code Security Report found that AI now authors roughly half of all committed code, and its average security pass rate has been stuck at 56 percent for a year. Most QE pipelines still were not built to catch the difference between code that runs and code that is safe.
No 50-page proposals. We'll tell you which level fits your situation, what a realistic engagement looks like, and what it would cost — in one direct meeting.
Thomas leads 10decoders' AI engineering practice and sits in on the scoping call himself — so the person mapping your engagement is the one who has shipped it before. His teams build and deploy agents for mid-market healthcare and fintech companies, with enterprise grade build experience for clients like IBM, Dedalus and Harris Healthcare. He'll be straight with you about what's worth doing and what isn't.
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