01
Product thinking and AI engineering in the same team.
We understand products, not only models. The question is always what a person will use, not what a model can do.
divum.ai
Applied AI
Applied AI
engineering.
We turn AI ambition into products people use and systems businesses can depend on. From the first idea to the work beyond launch.
In production atBoschMaruti SuzukiUltraTechTaneiraNykaa
Scroll01 · Who we work with
Three situations we are built for.
Each has its own first step, scoped and priced before any work starts.
01
Product teams building something new.
We design and build the product with you: the experience, the application and the AI inside it. Shipped on a date, built to scale.
VP Product · founders · CTOs→ AI product engineering
02
Enterprises putting AI into production.
We take one priority workflow into production inside your existing systems and controls, with quality and cost measured.
CIO · CAIO · GCC heads · transformation leaders→ Enterprise AI engineering
03
Engineering teams modernising what they run.
We migrate existing applications in reviewable slices and add AI to what you keep, without a rebuild.
CTO · heads of engineering→ Modernization with AI
02 · What we offer
Six engineering services. One standard.
Every one of them is delivered AI-first: the same AgileAI method, the same AI-assisted engineering, the same evidence before release.
01AI product engineering02Product engineering03Enterprise AI engineering04Data and knowledge engineering for AI05AI production engineering06Modernization with AI
01
AI product engineering
AI-native products and AI features, from discovery to scale. Product thinking, experience design and full-stack delivery with the model as one component.
First step · AI product opportunity sprint 02Product engineering
New products and platforms on web, mobile and cloud: .NET, Python, Node, React, native mobile. Eighteen years of shipping, now with AI in every team's toolchain.
First step · Discovery and architecture sprint 03Enterprise AI engineering
Agents, workflows, control towers and knowledge systems integrated with ERP, documents and operational data, governed and traceable.
First step · Production readiness assessment 04Data and knowledge engineering for AI
Pipelines, ontologies and knowledge graphs, permission-aware retrieval and edge deployment, so the enterprise's own data can answer questions and drive agents.
First step · Data readiness review 05AI production engineering
Evaluation sets, LLMOps, security controls, observability and cost ceilings: the discipline that keeps an AI system trustworthy after launch.
First step · Architecture review 06Modernization with AI
CodeFlo-led migration of existing applications, one reviewable slice at a time, then AI-enablement of what you keep.
First step · Modernization assessment03 · Work
What we have built.
Selected work, grouped by business function. Each links to the engineering story.
Floor plans, rule-checked5,114
Design and engineering · Building materials
Five thousand buildable floor plans, valid by construction.
For a national building-materials brand's home-builder app: 5,114 Vastu- and code-compliant floor plans, every one rule-checked and scored. Change a rule and the whole library regenerates in minutes.
Where prompting a model failed, the rules went into code and AI did only the skin.
Company record · client name withheld · 3D elevation pipeline in validationRead the engineering story →Finance and compliance · Bosch India
Invoices read by a model, checked by code.
GST and MSME registry validation for Bosch India. 99% reading accuracy.
Company recordRead the engineering story →GSTRegistry validated
MSMERegistry validated
99%Reading accuracy
More systems in production.
Customer and service operations
A voice agent that stays on script and on record.
Warranty-renewal calls for Maruti Suzuki. 72% contact rate; every turn traced; callbacks resume with full context.
Company record · Co-Innovation partnerPeople
Performance reviews for 20,000 employees, with zero data egress.
For UltraTech Cement, on-premises on an Edge Cluster inside the customer's own perimeter.
Company record · private deploymentExecutive knowledge
Hundreds of gigabytes of leadership documents, answerable.
Knowledge agents over an executive document estate, on the customer's own cloud tenant with a bring-your-own-model architecture.
Company record · unnamedOperations
Control towers that reconcile what the plant records with what the plant does.
Raw-material control towers, equipment-failure prediction, lab QC and cost control, integrated with SAP, planning systems and plant historians.
CapabilitySupply chain and retail
Shared workflows across organisations.
Procurement, inventory and quality on ⦃param⦄ Network for Taneira and Nykaa.
Company record · outcomes in v2Engineering
A migration the client could read before it ran.
Java Struts to Spring Boot through CodeFlo for a global automotive OEM. 60 to 80% lower migration cost.
Company record · client name withheldProduct
Platforms that carry a business.
A high-traffic corporate web platform, a consumer app for a retail brand, a UPI switch, a connected-vehicle service platform.
Capability · named on requestIndustries we have worked inManufacturing and materialsAutomotive and mobilityBFSI and fintechRetail and consumerTelecom and mediaPublic sectorAll work
04 · Why Divum
Six reasons a buyer picks us.
Not six slogans. One operating model: the people who shape the idea stay accountable for the working system.
02
The same engineers from prototype to production.
No hand-off from a lab to a delivery team. The people who shape the idea carry it through architecture, evaluation, integration and operation.
03
Full-stack means the whole system.
Experience, application, backend, data, cloud, AI, security and observability, engineered together.
04
AI where it adds intelligence. Deterministic engineering where it adds reliability.
Rules and calculations stay in code. Models are used for judgement, tested against the cases that matter, and bounded by cost and approval.
05
You own the system.
No lock-in to one model or one cloud where your requirements permit. Your architecture, your keys, your perimeter if you need it.
06
Eighteen years of shipping through platform shifts.
Mobile, cloud, the modern web, machine learning, connected enterprises, now agents. The AI chapter is a continuation, not a pivot.
IdeaProduction
IdeaOne teamProduction
05 · How we engage
Small first. Then a team for as long as the work deserves.
No theatre. The first engagement ends in a concrete decision and a working slice of the real system.
01
One question
Bring the product idea, workflow bottleneck or architecture problem.
02
Fixed-scope first step
A sprint, readiness assessment or architecture review, scoped and priced before work starts.
03
Production pilot
One workflow. One success metric. One cost ceiling. One security model.
04
Dedicated team
Forward-deployed applied AI and product engineers, embedded with yours and scaled by the quarter.
Scale,
redesign
or stop
redesign
or stop
First step
Start with a fixed-scope step.
A sprint, a readiness assessment or an architecture review, scoped and priced before work starts, ending in a decision you can act on. It is engineered as the first slice of the real system.
Then
Continue with a dedicated team.
Forward-deployed pods of applied AI engineers and product engineers, embedded with yours, scaled by the quarter, on time and materials. This is how most of our longest-standing clients work with us.
The production pilotOne workflow. One success metric. One cost ceiling. One security model. One decision at the end: scale, redesign or stop.
06 · Accelerated by our own platforms
Built on eighteen years of product engineering. Accelerated by our own platforms.
Everything we learn in engagements becomes the platform. Nothing in the platform exists that was not first proven in an engagement.
Agentic AI platform
⦃param⦄ ai/Studio
Governed agentic AI for the enterprise. Every workflow compiled to a versioned MindSchema: traceable, reproducible, cost-bounded, model-agnostic, deployable inside your perimeter with zero data egress.
paramai.studio ↗Modernization tooling
CodeFlo
Persistent, git-backed engineering sessions that produce a reviewable migration plan with per-file traceability.
Modernization with AI →Supply chain platform
⦃param⦄ Network
Procurement, logistics, inventory, quality and control towers shared across organisations, on a patent-filed universal commerce state machine.
param.network ↗ai/Studio, CodeFlo and ⦃param⦄ Network are available by subscription where they fit, with the engineering team behind them.
07 · Perspectives
From our engineers.
Bylined, each anchored to a system in production.
AI-first digital transformation
Why your GenAI pilots are not reaching production, and the six things that change it
For CIOs, CAIOs and GCC heads.
Divum engineering · coming at launch
AI product engineeringBefore the model, find the moment
For product leaders, founders and CTOs.
Divum engineering · coming at launch
Applied GenAI, with proofThe invoice reader: two model calls, four deterministic steps, one accuracy figure
For everyone evaluating us.
Divum engineering · coming at launch
What's next
What needs to work in your world?
A product, a workflow, a knowledge base, a platform that needs to change. Bring the problem. We will work through the engineering and tell you plainly whether it is worth building.
