Anusamhita Solutions - AI-Augmented Enterprise Transformation

Why We Exist

Every enterprise adopted AI. Almost none of them architected for it.

Your developers are using copilots, code generators, and autonomous agents every day. But the AI has no idea what your company actually needs.

Product requirements sit in docs that never reach the model.
Engineering standards exist on paper but not in the context window.
Security and compliance rules are enforced in review - long after the AI already wrote the code.
Every developer prompts differently. Every output is a gamble.

“The gap isn't in the AI. It's in what the AI knows about you.”

You can't orchestrate AI on a foundation that doesn't exist.

Many enterprises are trying to adopt AI without the foundational Cloud data layer, governance, or operational discipline to support it. The result is fragmented data, ungoverned models, and AI investments that don't compound.

AI without a governed Cloud foundation is a house of cards.

Before AI can deliver enterprise value, you need the right data platform and governance backbone in place. A Databricks-powered Lakehouse with governance embedded from the start is not a nice-to-have - it is the prerequisite.

Data scattered across silos can never feed a coherent AI strategy.
Without governance, every model you deploy is a compliance risk.
Operational runbooks, SLAs, and heartbeats separate production AI from science experiments.

This isn't an incremental improvement.

It's the missing infrastructure layer for AI-native development.

The context window is the new control plane.

AI models are only as useful as the context they receive. Today, that context is fragmented, inconsistent, and manually assembled - if it exists at all.

Agents without context produce code that compiles but violates your architecture.
Teams without shared standards drift apart at the speed of AI.
Enterprises without orchestration accumulate technical and compliance debt silently.
The longer you wait, the harder it is to unwind.

We build the layer between your enterprise brain and your AI tools.

Anusamhita Solutions exists to solve one problem: making sure every AI agent in your organization operates with the right context, every time.

We encode your global standards into a persistent, enforceable context layer.

We structure your team-level requirements into auto-loading PRPs.

We orchestrate what reaches the context window - and when.

We give you governance and visibility over every AI-assisted output.

We don't just advise on AI transformation.

We build the infrastructure that makes it reliable.

Proven at Enterprise Scale

We've done this before.

Anusamhita leadership has driven AI and Cloud transformation at two of the world's most demanding enterprises. These aren't case studies - they're the systems we built.

Starbucks

DeepBrew

We drove the end-to-end design, development, and delivery of Starbucks' DeepBrew Recommendation Platform - the AI engine behind personalized experiences for 30 million users, generating 6 billion product recommendations annually.

We built the next-generation AI/ ML Cloud platform from the ground up with enterprise-grade security and safety guardrails baked in from day one - not bolted on after launch.

30M

Active Users Served

Recommendations / Year

0-to-1

AI/ ML Platform Built

Day 1

Security Baked In

General Motors

Insight Factory

We built the Insight Factory and served as GM's innovation lighthouse - creating the "Path to Production" framework from scratch that became the gold standard for shipping AI into production safely and at speed.

We authored and maintained Gold Standards of Engineering across Frontend, Backend, and Data Science - spanning Python, JavaScript, React, Angular, and Databricks - ensuring every team shipped code that met enterprise-grade quality, security, and stability bars.

300%

Fewer Prod Incidents

2.5X

Faster Innovation

200%

Dev Productivity Boost

Security Compromises

Our Offerings

Two complementary offerings that take enterprises from zero AI foundation to fully governed, context-aware AI operations.

Offering #1

The ASDLC Platform Suite

Agentic SDLC, governed. Following one law - Agents draft, humans decide.

AI agents now draft most of the code, documents, and designs in an engineering project. Traditional SDLC assumed a human wrote every line, so its checkpoints stayed implicit, social, and unenforced. Without an explicit decision layer, agentic output reaches production faster than anyone can be accountable for it.

No ASDLC framework in operation means AI slop reaching into Production - And with it, a rising risk of disasters that cost money and reputation alike.

Why it exists

Agentic Speed Needs a Governed Path

Agents give speed. The Golden Artifacts decide whether that speed compounds into value or into debt.

Agents Draft. Humans Decide.

Agents produce every draft. A named accountable human signs off each Golden Artifact.

One Phase. One Gate.

The lifecycle runs strictly in order. No phase starts until the gate before it closes.

See All. Change Nothing.

Every governed project in one read-only view: Progress, gates, owners, and approvals.

At a glance

Inside ASDLC

Four components, four distinct jobs - Together, a working governed robust platform.

The law

Governance Standard

The single source of truth for your Golden Artifacts and engineering standards, with human-only approval built in.

The mold

Project Blueprint

The starting point for every new project, with the lifecycle, artifacts, and guardrails built in. Every AI agent works to the same rules. Compliant from commit zero.

The specimens

Reference Projects

Working reference projects across application and data engineering stacks, captured at different lifecycle stages with real approvals. Proof the law holds on real code.

The instrument

Oversight Console

An organization-wide, read-only view of phases, gates, owners, approvals, and findings across every governed project.

The shared vocabulary

Six Phases, No Unowned Decisions

Every artifact answers a question that will be asked eventually - Cheapest on paper before the work, not in production at 2 AM.

Discovery

Is this the right problem, with a named owner and a clear business case?

Prevents building the wrong thing

Design

Is the solution sound, secure, and responsible with data before a line is written?

Prevents building it wrong

Build

Is the plan approved, the value still real, and the team cleared to proceed?

Prevents runaway construction

Quality

Is it maintainable, secure, and ready for the team that will run it?

Prevents shipping fragility

Operations

Has it been proven to fail safely, recover fast, and stay supported?

Prevents learning failures in production

Handoff

Has the receiving team formally accepted ownership?

Prevents the orphaned system

Engagement path

From an AI-Reactive to an AI-Native Organization

Governed in week one. Runs inside your existing engineering platform - No new cloud account, database, or AI vendor credential required.

Establish & Equip

We set up the ASDLC foundation inside your own engineering environment and encode two layers of law: Industry best practices, and the local standards unique to your organization.

Gate & Enforce

We switch on the gate across the organization, so every project is held to the same rules automatically. From then on, no project skips a phase and no decision goes unowned.

Launch & Scale

Your first real project starts governed - Protected, gated, and owner-reviewed - And passes on its first push. From one project to the whole organization - The same law, scaled team by team.

Agents give speed. Our ASDLC ensures speed doesn't come at the cost of quality.

Agentic superpowers are here for every organization willing to use them well. ASDLC lays the engineering discipline underneath that speed and brings leadership along at every step - So engineers move faster, leaders stay in control, and the whole organization becomes AI-native together.

Embrace AI superpowers Responsibly

Our Team

Led by experienced transformation leaders, powered by AI-leveraging ninjas delivering 10X output with safety nets on

Founder & CETO

Pritesh Soni

Pritesh Soni is a founder and AI-augmented engineering transformation leader who blends deep hands-on execution with senior business judgment to deliver measurable enterprise outcomes. His work is rooted in a business-first philosophy: that software, data, and AI matter only insofar as they compound value for customers, operators, and decision-makers. Pritesh specializes in translating ambiguous business problems into clear operating models where human leadership is amplified by AI-accelerated analysis, engineering, and delivery.

Across his career, Pritesh has led multiple 0-to-1 and scale journeys, helping companies such as Skellam AI and Locus.sh build AI-ready platforms, mature engineering disciplines, and outcome-driven delivery systems. Previously, as a Strategic Thought Leader at EXL, he helped design and execute AI-led transformation programs - strengthening data engineering foundations, operationalizing machine learning, and enabling AI-driven revenue and productivity gains for global enterprises.

At Anusamhita, Pritesh brings a builder-led, AI-augmented approach - combining leadership, ownership, and accountability with a relentless focus on clarity, user experience, and quantified impact. His leadership philosophy centers on humility, inclusiveness, continuous learning, and the belief that empowered people, equipped with AI leverage, create durable transformation.

AI-Augmented Delivery Force

The Anusamhita Ninjas

Our ninjas are ECO-trained engineers and architects who use AI as a force multiplier - not a crutch. They deliver at 10X velocity while keeping enterprise guardrails, compliance, and quality standards firmly in place.

Deep expertise in foundational platforms like Databricks and Atlan, combined with SRE discipline for proper instrumentation, heartbeats, operational runbooks, and SLA management. Every ninja is adept across leading agentic tools - Claude, ChatGPT, Copilot, and autonomous coding agents - turning AI capability into governed enterprise outcomes.

Databricks & Lakehouse

Standing up enterprise-grade Databricks environments, medallion architectures, Unity Catalog, and the data foundation that powers AI/ ML workloads.

Atlan & Data Governance

Deploying governance frameworks with automated cataloging, lineage tracking, policy enforcement, and access controls - baked in from the start.

SRE & Operational Excellence

Proper instrumentation, heartbeat monitoring, SLA tracking, operational runbooks, SOPs, and escalation paths - ensuring production systems run reliably.

Agentic AI Tools

Adept across modern agentic platforms - Claude, ChatGPT, Copilot, and autonomous coding agents - using AI as a force multiplier across every workflow.

AI-augmentedTransformation.