VSutra AI

Production AI

Production AI systems for workflows that need more than a demo

Support, knowledge, agents, and custom applications for teams who need AI to work on real data, with a plan for when it is wrong. Start by finding out whether your workflow is ready.

Book an AI readiness call

Demo AI vs production AI

Where pilots usually stop

Most AI pitches look identical until you ask what happens on real data and real mistakes.

  • DataDemo: A clean sample someone prepared for the pitch.Production: The real, scattered, partly out-of-date material your team actually works from.
  • Wrong answersDemo: Rare, and quietly skipped over in the walkthrough.Production: Expected. Answers are checked against sources and uncertain ones go to a person.
  • OwnershipDemo: Unclear who maintains it after the pilot.Production: Agreed up front: who owns the model, the data, and the monitoring.
  • ImprovementDemo: Frozen at launch.Production: Corrections from real use feed back into what the system knows.

Services

Seven ways we put AI into a workflow

Each service page states the buyer problem, what we build, what we need from you, and a sensible first engagement.

AI Consulting & Strategy

A readiness read on one workflow before you pick a vendor or commit budget.

Custom AI Applications

LLM-powered tools built into the systems your team already works in.

AI Agents & Automation

Agents for the steps that need judgment, with limits and human checkpoints.

Enterprise Knowledge Assistants

Source-backed search and chat over your documents, wikis, and knowledge bases.

AI for Customer Support

Support assistants that look up, check, and hand off, built on SupportWiz.

Predictive Analytics & Anomaly Detection

Forecasting and anomaly flags on top of data you already trust.

AI for FinTech & Trading Data

Analytics and anomaly detection on trade and account data, with human review.

See all services

Product

SupportWiz: the production AI loop

AI support assistant that answers from current company knowledge and improves with human correction.

  1. 01

    Source

    Start from current company knowledge, not general model memory.

  2. 02

    Answer

    Generate a response grounded in the retrieved material.

  3. 03

    Validate

    Check the response against its sources before it goes out.

  4. 04

    Correct

    A person fixes anything wrong or incomplete.

  5. 05

    Learn

    Corrections update what the system knows for next time.

See SupportWizAI for customer support

Proof

SupportWiz / ACCR support enablement

Adaptive knowledge assistant for customer-facing teams: source-backed answers, validation, and a correction loop.

A client-confirmed metric for this work has not been published, so it is shown as a case narrative rather than a result.

Read the case narrative

Before you talk to any vendor

Self-qualify first

Read how to judge an AI vendor, work through the readiness checklist, then book a call if it still makes sense.

How to Evaluate an AI Vendor Before You Sign Anything

Most AI vendor pitches sound identical once the buzzwords are stripped out. What to actually ask before committing budget: what data they need, how failure is handled, and who owns the model afterward.

What “AI Readiness” Actually Means for a Mid-Size Business

It isn't about having a data science team. It's about knowing where your data lives, who's allowed to see it, and whether anyone would trust an automated answer over a human one yet.

Open the AI readiness checklist

Not sure your workflow is ready?

Describe it. We will tell you whether it is ready, risky, or not worth automating yet.

Book an AI readiness call