Innovista Labs/Web · Software · AI

Don't buy another SaaS tool.
Automate the work.

We build custom AI autopilots for high-growth companies. From customer support agents that actually resolve tickets to internal 'Second Brains' that organize your enterprise data. We manage the intelligence, you get the outcome.

100+
Projects delivered
End to end
Design, build, deploy
24/7
Support & maintenance
ReactNext.jsNode.jsPythonLangChainLlamaIndexPostgresPineconeOpenAI.NETFlutterAWSAzureDockerKubernetesWordPressShopifyGraphQL
ReactNext.jsNode.jsPythonLangChainLlamaIndexPostgresPineconeOpenAI.NETFlutterAWSAzureDockerKubernetesWordPressShopifyGraphQL
01What we offer

Three distinct outcomes to scale your business.

Whether you need autonomous support, a unified knowledge base, or voice intelligence, we provide the complete engineering team to build and run it.

02About the practice

A specialized firm that builds
the intelligence running your business.

Innovista Labs works with companies that have outgrown off-the-shelf tools. Most arrive with disjointed software, manual workflows, and a stack of documents nobody can search.

We replace that with AI autopilots built for how the business runs, and we stay on afterwards to keep them running.

01

We sell outcomes, not copilot tools.

The era of buying a software tool and forcing your team to learn it is ending. The future belongs to AI autopilots. We act as your AI engineering partner—auditing workflows, building the custom AI infrastructure, and managing it so your team can focus on high-judgment work.

02

Autonomous, not just automated.

Our agents don't just summarize FAQs—they integrate directly with your CRM and backend to check order statuses, process returns, and resolve issues 24/7. We turn support centers from cost centers into resolution engines.

03

AI grounded in your own data.

Our RAG engines retrieve from your documents, policies and records before answering, so responses cite something real. No hallucinated pricing, no invented policy. We build a permission-aware intelligence layer.

How an engagement runs
  1. 01
    Audit
    Workshops, data review, scope
  2. 02
    Design
    Agent flows, intelligence layer
  3. 03
    Build
    AI infrastructure, integrations
  4. 04
    Run
    Deploy, monitor, scale
03Case Studies

Real outcomes delivered for scaling businesses.

EdTech01
ThePrepLab logo

ThePrepLab

thepreplab.in

Built their complete Learning Management System, an AI studybot for instant doubt resolution, and an internal marketing & sales CRM.

  • LMS
  • AI Studybot
  • Internal CRM
Healthcare AI02
Allay AI logo

Allay AI

allayai.com

Built HIPAA-compliant voice intelligence for doctor-patient conversations in the USA, alongside a RAG engine for insurance automation.

  • Voice Intelligence
  • HIPAA Compliant
  • RAG Automation
E-Commerce03
Personix AI logo

Personix AI

personixai.com

Context-aware AI chatbots for e-commerce customer service, built on high-precision RAG engines that resolve tickets autonomously.

  • RAG Chatbot
  • Customer Service
  • Context-Aware AI
04By the numbers
100+
Projects delivered

Websites, custom software and AI integrations shipped across manufacturing, healthcare, logistics and retail.

50%
Faster to launch

One team covering design, build and the AI layer removes the handoffs that usually stretch a project by months.

24/7
Support after launch

Monitoring, patching and a named contact — the relationship does not end at the deployment.

06Start here

Tell us what is
not working yet.

A website, an internal system, an AI layer over documents nobody can search — or all three. Send a couple of paragraphs and we will come back with an honest read on scope, cost and whether we are the right people for it.

Useful to include
What you have nowSite, tools, spreadsheets, systems
What it should doThe outcome, not the feature list
TimelineWhen it needs to be live
Budget rangeEven a rough band helps us scope