divum.aiApplied AI
engineering
Start with one question

How we engage

Small first. Then a team for as long as the work deserves.

We do not ask for a long commitment to find out whether something is worth building. Every engagement starts with a fixed-scope step and continues, if it earns it, with a dedicated team.

Four stepsFixed → Pod
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

First steps · fixed scope

Six ways to start.

Each is scoped and priced before work starts, delivers the artefacts below, and ends in a decision.

01

AI product opportunity sprint

Opportunity map, feasibility, experience prototype, architecture, and a decision.

AI product engineering →
02

Discovery and architecture sprint

Scope, architecture, delivery plan and a commercial proposal you can take to your board.

Product engineering →
03

Production readiness assessment

One initiative across architecture, data, evaluation, security, operations and total cost. Ends in scale, redesign or stop.

Enterprise AI engineering →
04

Data readiness review

Sources, gaps, access boundaries and the first use case, with an architecture and a plan.

Data and knowledge engineering for AI →
05

Architecture review

Your system against six lenses, with findings you can act on.

AI production engineering →
06

Modernization assessment

Estate map, options and trade-offs, one representative slice migrated with tests.

Modernization with AI →
The production pilotOne production workflow. One agreed success metric. One cost ceiling. One security model. One decision at the end: scale, redesign or stop.

Engineered as the first slice of the real system, never as a throwaway.

Dedicated teams

Pods that own the outcome.

Forward-deployed pods of applied AI engineers, product engineers and data engineers, embedded with your teams and led by an engagement lead who owns the outcome. Scaled by the quarter, on time and materials. Founder-level attention on every account. India delivery economics with US contracting through our Delaware entity.

Programmes

Phased so you can see it.

Integration, governance, rollout and change management for enterprise initiatives, phased so you can see and use progress every sprint.

How we propose

We answer the vendor assessment before you send it.

Every first step produces the documents an enterprise needs to decide, in a form your architecture, security and procurement teams can review without a meeting.

Business requirements document

the workflow, the baseline, the owner, the measure of success

Approach and architecture

systems touched, data flows, model choices, approval points, deployment topology

Technical proposal

scope, phases, team, assumptions, what is in and what is out

Security controls

identity, perimeter, egress, logging, the questionnaire answered

Infrastructure bill of materials and TCO

what it runs on, what it costs to run

Model cost estimate

tokens, runs, ceilings, and the levers that change them

Delivery plan

sprints, milestones, decisions, and the date you see the first working slice

Every first step produces these, in a form your architecture, security and procurement teams can review without a meeting.

Start with one question →

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.