Today · 2026

Software now performs work. We build the systems that let it do so safely.

Divum is a solutions company with eighteen years of product engineering behind it. The AI shift did not change what we are. It changed the size and complexity of the engagements we take on, and it gave us a reason to productise what we had learned.

Three things happen under one roof: applied AI engineering, the product and platform engineering that has always been our core, and the two accelerators we built from our own work. Keeping them together removes the hand-offs, vendor coordination and integration risk that enterprises dislike most.

The AI practice has its own home at divum.ai. This page is the view from the company that built it.

Solutions we build

Outcomes first. Each family below is in production with at least one large enterprise, and each runs on the same engineering practice.

Agentic and GenAI solutions

Voice and text agents for sales, service and support, with context that survives across calls
Document intelligence: invoices, drawings, contracts and quotes read into structured, validated records
Knowledge and analytics over enterprise data, using graph-first retrieval where vector search breaks down
Code migration and modernisation with persistent, reviewed AI coding sessions
On-premises and air-gapped deployment through an Edge Cluster when data cannot leave

Digital products and platforms

New product development from concept and user research to launch, for startups and enterprise ventures
Application modernisation and workflow automation for existing estates
Mobile, web and cloud engineering across AWS, Google Cloud and Azure
Data engineering: warehouses, lakes, pipelines, MLOps
Design systems and user experience with the discipline of a team that has watched app markets mature

Supply chain and multi-enterprise

Procurement, EXIM, inventory, quality and logistics workflows shared across organisations
Control towers over orders, approvals and documents with a cryptographic audit trail
ERP integration without a central data custodian
Applications generated to fit a customer’s exact process rather than configured within a vendor’s limits

Two accelerators, built from our own work.

Neither began as a product to sell. ⦃param⦄ came out of years of building order and supply chain systems for customers. ⦃ai/Studio⦄ inherited its orchestration patterns and added the governance enterprises asked for once agents reached production.

⦃ai/Studio⦄ paramai.studio

An enterprise agentic AI platform where every workflow is a versioned MindSchema. Intelligence is spent at design time, when a Mind Designer reasons about the problem, writes custom operators and picks a model per step. At runtime the enterprise gets a contract it can read, diff and audit.

TraceableEvery operator execution emits structured events; the audit trail is a property of execution, not a logging add-on.

ReproducibleSame schema, same inputs, same execution path, even when individual steps use language models.

Cost-boundedCost is estimated before a run and capped per Mind. No runaway spend.

Model-agnosticGemini, Claude, GPT and open models routed per step; bring your own model or vector store.

Edge ClusterExecution inside the customer’s environment with governance events synced back. Zero data egress when required.

Visit paramai.studio

⦃param⦄ Network param.network

A multi-organisation supply chain operating system. Every enterprise workflow maps to one five-phase lifecycle, Initiation, Agreement, Execution, Settlement, Completion, which is the basis of a patent filing with the Indian Patent Office and the reason applications can be generated to fit a process exactly.

Shared recordBuyers, suppliers, forwarders and finance teams work on one transaction with per-organisation cryptographic identity.

Selective replicationDocuments reach only the organisations that are party to them.

ERP in, ERP outAn event-driven gateway with client-specific transforms, built for thousands of documents a minute.

Intelligence layer⦃ai/Studio⦄ Minds automate state transitions, classification and reconciliation on top of the network.

Visit param.network

How we work now

AI generates fast; humans validate deep. We call the method AgileAI, and it is as much about what we refuse to let a model do as what we let it do.

Structured sprint context

Every sprint carries a context directory: requirements, decisions, constraints, test data. AI-assisted development works from that record, not from memory, and so does the human review that follows.

Deterministic where it matters

Language models never touch geometry, money or compliance logic directly. A deterministic engine and a validator gate the output. Verification harnesses sweep every case before integration.

Phased, visible delivery

A fixed-cost first phase that customers can touch and feel, then time-and-materials as scope settles. One rate per person, not a pyramid.

In production

Customers cleared for public reference. Others are described without names on the work page.

99^%

Bosch IndiaInvoice reader with GST and MSME validation.

72^%

Maruti SuzukiWarranty-renewal voice agent contact rate.

20,000

UltraTech CementEmployees on an HR Mind with zero data egress; 600+ GB on Azure AI Foundry.

2

Nykaa, Tata Taneira⦃param⦄ dashboards generated by ai/Studio Minds.

Platforms and partners

We build on the major clouds and model providers and keep the architecture portable. Partner programmes give us early access; the customer keeps the choice.

Google Cloud and GeminiMicrosoft Azure and AI FoundryAWSAnthropic ClaudeOpenAIOracleSAP

ComplianceISO/IEC 27001 certified ISMS; SOC 2 Type II in progress. Delivery experience with HIPAA, GDPR and DPDP, PCI DSS and FDA system testing. UNECE R155/R156 for connected vehicles.

DeploymentCloud-agnostic, with on-premises Edge Cluster for data-sovereign workloads.

MarketsIndia, with growing engagements in the United States and South-East Asia through Divum Inc. (Delaware).

Team100+ engineers in Bengaluru across AI, product, data and platform engineering.