Built at Squibb

Built at Squibb

What we’ve built.

Not decks. Working systems.

Every recommendation we make to a client comes from patterns we’ve tested on ourselves first. The systems below are internal builds: real architecture, real code, real constraints. The design patterns from these builds transfer directly to client engagements across any domain.

A working view of the data platform architecture we think in. From ingestion through analytics to agentic workflows.

How we think

Architecture first. Always.

Most AI investments break because they start at the intelligence layer and assume the data will sort itself out. We start at the other end. Data sources, ingestion, platform, governance, semantic layer. Then analytics. Then AI and automation. Then agentic workflows. Every layer tested before the next one goes on.

The systems on this page follow that sequence. They are not prototypes or experiments. They run in production, serve real users, and operate under real cost constraints.

What we’ve built

Systems in production. Patterns that transfer.

System 01

Intelligent Workforce Platform

What it is

A people management system with embedded AI capabilities, built on a tiered LLM routing architecture.

Architecture highlights

Tiered LLM routing that selects the appropriate model based on task complexity and cost profile. Tenant-scoped agent configuration with isolated data boundaries. Cost-aware AI deployment that achieves enterprise-grade capabilities without open-ended API spend.

What it proves

Production AI is achievable without production-scale budgets. The right routing architecture makes intelligent features commercially viable across multiple tenants.

Transferable patterns

Tiered intelligence routing. Cost-aware inference. Tenant-scoped AI isolation.

System 02

Agentic Systems for Regulated Contexts

What it is

A healthcare-oriented platform with patient-facing AI capabilities, built for a domain where accuracy and auditability are non-negotiable.

Architecture highlights

Agentic patterns for autonomous task handling within defined boundaries. Tenant-scoped configuration allowing per-context customization. Traceability and audit trails designed into the agent layer, not added afterwards.

What it proves

Agentic AI works in environments where getting it wrong has real consequences. Reliability and traceability come from architecture decisions, not from bolting compliance onto a prototype.

Transferable patterns

Agentic workflow design. Regulated-context AI deployment. Audit-ready agent architecture.

Framework

The Pattern Library

What it is

The architectural decisions from our internal builds, extracted and documented as reusable design patterns for client engagements.

Architecture highlights

Tiered intelligence routing for cost-effective LLM deployment. Tenant-scoped agent configuration for multi-client isolation. Cost optimization frameworks that make AI commercially sustainable. Event-driven pipelines for real-time data flow.

What it proves

The value of internal builds is not the builds themselves. It is the transferable patterns they produce. Every client engagement benefits from architecture that has already been stress-tested.

Transferable patterns

All of the above, applied to your domain.

What we design for

Principles, not preferences.

Cost-Aware AI

Tenant Isolation

Pattern Reuse

Production by Default

Why it matters

Your engagement starts where our builds left off.

Every architecture decision on this page was made under real constraints: real budgets, real users, real deadlines. That is why these patterns transfer. They were never theoretical.

When we scope a client engagement, we do not start from a blank whiteboard. We start from architecture that has already handled cost optimization, tenant boundaries, agentic reliability, and production deployment. Your project benefits from the engineering hours we have already invested.

This is also how we think about the relationship between our two practices. The technology and data practice builds the systems. The learning and development practice ensures the teams using those systems are equipped to operate them. Both draw from the same architectural thinking. Both are available in every engagement.

These patterns work in your domain too.

Whether you need data architecture, applied AI, or team capability programs, the engineering behind what you have seen on this page is what we bring to every engagement. Tell us what you are working on.

Talk to us

About

Squibb Consulting is a senior-led firm specializing in enterprise data architecture, applied AI systems, and learning innovation. Founded in 2017. Headquartered in Pune, India. Serving clients across the US and India.

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Contact info

We help organizations modernize data, build digital products, and scale internal capabilities, with clarity and purpose.

  • #1028, DNK Square, Viman Nagar, Pune, MH, India 411014
  • (+91) 95117 88458
  • contact@squibb.in