Agents aren’t the hard part. Your data is.
Agentic platform consulting and AI agent development, built on the data engineering 90% of firms still lack.
Platform anatomy
- Layer 01Foundation
Unified, processed, governed data.
- Layer 02Context
The semantic layer agents reason over.
- Layer 03Agents
Orchestration, tools, and guardrails.
- Layer 04Interfaces
Where people meet the platform.
The gap
90%
of firms can’t leverage agents. The models aren’t the problem.
Agents need one path to truth. Most firms have four disconnected systems — and no way for an agent to act on any of them reliably.
Data estate
Fig. 01 — no shared path
Agent reaches nothing it can trust
Why the path fails
Siloed
Your CRM, ERP, warehouse, and spreadsheets each hold a piece of the truth. No agent can act on data it can’t reach.
Unprocessed
Raw records aren’t facts. Without pipelines that clean, join, and model your data, agents inherit every inconsistency.
Ungoverned
When “revenue” means three different things in three systems, an agent’s confident answer is a liability, not an asset.
In practice
One month of data engineering beat seven months of agent tuning.
A Fortune 100 fintech tried to automate invoice OCR and accounting entries with AI agents. Results stayed mediocre — until the data underneath was rebuilt.
Fortune 100 fintech · Accounts payable — invoice OCR and posting
Before — agents first
7 months
Agents automating invoice OCR and posting, built straight on the raw data estate.
Outcome
Mediocre accuracy. Never trusted in production.
After — foundation first
6 weeks
1 month Clean datasets and sources of truth, engineered first.
2 weeks The full agent workflow, deployed on top.
Outcome
Near-perfect production accuracy. It outperformed the seven-month attempt in two weeks.
Since then, a new agent has shipped on that foundation every two weeks.
The platform
Four layers. Built in order. No shortcuts.
An agentic platform is infrastructure, not a demo. Each layer earns the next.
Services
From scattered systems to a platform your agents — and your people — can trust.
Data Readiness
Agents can only act on data they can reach and trust.
We audit what you have, unify what’s siloed, and process what’s raw — pipelines, warehousing, semantic modeling, and governance that turn scattered systems into a foundation agents can work on.
Agentic Platform Engineering
Not a chatbot bolted onto a database — a platform.
We design and build the agent layer on top of your prepared data: orchestration, tool interfaces, retrieval, evaluation, and the guardrails that let agents do real work inside your business.
Integration & Operations
Shipped is the beginning, not the end.
We integrate the platform into daily workflows, automate the reporting it replaces, and run it with you — monitoring, iteration, and enablement so your team owns the system with confidence.
Pedigree
We learned this at the hardest scale there is.
15+
Years of data engineering
Building pipelines, warehouses, and data platforms for Fortune 500 companies.
Google · Nasdaq
Where we learned scale
Careers spent engineering data systems at some of the world’s most demanding companies.
NYC
Founded in New York
A consulting practice built on one mission: turn information into decisions.
Questions we hear
Straight answers on agentic platforms.
What does Finsight Analytics do?
We build agentic AI platforms on a foundation of data engineering — unifying siloed or unprocessed data so companies of all sizes can run production agents, not demos.
What is agentic platform consulting?
Engineering across four layers: foundation data, semantic context, agent orchestration with tools and guardrails, and the interfaces people use. Assess readiness, build in phases, hand the system to your team.
Why do AI agent projects fail?
Rarely because of the model. Most fail because the data underneath is siloed, inconsistent, or unprocessed. Preparing the data layer first is the difference between a demo and a platform.
Who is this for?
CTOs, VPs of data, and founders who want agents that work in production. We bring Fortune 500 data engineering discipline — careers at Google and Nasdaq — to companies of all sizes.
Where should we start?
Start with a readiness assessment of your data estate. That scopes whether the next move is foundation work, a first production agent, or a fuller platform build.
Next step
Find out where your data actually stands.
A readiness assessment maps your silos, your gaps, and the shortest path to a platform agents can work on. No slideware — an engineering answer.
Engineering answer · No slideware