SERVICES · AGENTIC AI

Agentic AI

Intelligent agents that automate decisions, workflows and enterprise processes inside real operations — grounded in your governed data, wrapped in guardrails, and measured against a business number rather than a demo.
OVERVIEW

Beyond chatbots.
Agents that do the work.

An assistant answers questions. An agent takes a goal, plans the steps, calls your systems and tools, checks its own work and finishes the job — with a human in the loop where it matters. XEqualTo builds agents for finance, operations, support and data teams that run inside your existing processes, on data you already govern.
We start from one workflow and one number: hours saved, tickets resolved, cost avoided. Then we build the agent, the evaluation harness and the guardrails, and only expand scope once the number moves.
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WHERE YOU ARE
WHERE YOU LAND
Chatbots that answer but never act
Manual workflows across five systems
AI pilots that never reach production
Models guessing over ungoverned data
Nobody knows what the AI did
OUR CAPABILITIES

Everything an agent needs
to run inside a real business.

Agentic workflow automation

Multi-step agents that take a goal, plan the work, call your systems and tools, and complete the process end to end — with human approval gates where the risk demands it.

Agent platform & architecture

Orchestration, tool integration, memory, retrieval and a guardrails layer designed as a platform — so the second and tenth agent are faster to ship than the first.

Evaluation & guardrails

Eval suites, policy checks, approval flows and rollback so agents are tested like software and behave predictably in production, not just in the demo.

Grounding in governed data

Retrieval over your lakehouse, catalog and semantic layer so agents reason over the same trusted numbers as your dashboards — with lineage on what they used.

Permissions, audit & safety

Least-privilege tool access, PII controls, full action logs and reviewable traces — so security, compliance and audit can sign off on agents in production.
OUR APPROACH

Scope, build,
evaluate, scale.

Agents fail when they are built as demos and scaled as hope. We scope one workflow against one number, build with evaluation and guardrails from day one, prove the result in production, and expand only on evidence.
01
Weeks 1–2

Scope

Pick the workflow, define the number it should move, and map the systems, data and approvals involved. Decide what the agent may do alone, what needs a human, and what it must never touch.
Workflow selectionSuccess metricTool & data mapRisk boundaries
02
Weeks 2–6

Build

Design the agent, integrate tools and retrieval over your governed data, and build the eval suite alongside it. Guardrails, logging and approval flows are part of the first version, not a later hardening phase.
Agent designTool integrationRAG over lakehouseEval suite
03
Before it goes live

Evaluate

Run the agent against real historical cases and a shadow period in production. Measure accuracy, cost per run, latency and escalation rate. Ship only when the numbers clear the bar you set in scoping.
Offline evalsShadow modeCost per runGo/no-go review
04
On evidence

Scale

Monitor the agent in production, tune on failures, and expand to adjacent workflows on the same platform. Every new agent inherits the guardrails, evals and audit trail of the first.
Production monitoringContinuous evalsAdjacent workflowsAgent platform
TECH STACK WE USE

Built on the data platforms
your agents will reason over.

Agents are only as good as the data under them. We build on Snowflake, Databricks, dbt and the major clouds, and integrate the leading model providers and agent frameworks on top.
AWS
Databricks
Microsoft Azure
Snowflake
Google Cloud
Power BI
Tableau
dbt
QUESTIONS

Things people ask
before building agents.

Not sure which workflow to start with? Talk to us directly — we would rather answer it properly than guess at it.
Contact us
A chatbot answers a question. An agent is given a goal, plans the steps, calls tools and systems, checks its own output and completes the task — escalating to a human when it hits a boundary you defined.
High-volume, well-defined, multi-system processes with a clear number attached: invoice matching, support triage and resolution, data pipeline incident handling, report generation, vendor onboarding. We help you pick one in the scoping week.
Least-privilege tool access, approval gates on risky actions, policy checks, eval suites run before every release, full action logs and the ability to roll back. Security and audit review the design before go-live.
Agents need governed, accessible data to reason well. If your platform is not there yet we scope the data work alongside — often that is where our Data Engineering and DataOps teams come in.
We are model-agnostic and pick per workflow on accuracy, cost and data-residency requirements — across the major model providers, on your cloud where needed.
With a free one-week workshop: we map candidate workflows, choose the first, define the number it should move and scope the build.

Ready to put an agent
to work?

We’ll come back with a candidate workflow, a success metric and a scoped plan for the first agent. No commitment beyond the conversation.

Contact us
Precision in Every Data Point
Newtown, Kolkata
One office, every time zone
connect@xequalto.com
+91 9147391433
OUR SERVICES
Data Engineering Data Architecture Agentic AI FinOps DataOps AIOps
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