About us

Welcome aboard! We're Wexa, the Enterprise AI Context Platform. We take the systems an enterprise already runs and make them AI-first, without anyone giving up control of their data. Ready for more? Keep scrolling.

It started in 2023 with one question: how do you make an enterprise AI-ready when its data lives in dozens of systems that were never meant to talk to each other? We didn't answer it on a whiteboard. We answered it inside real customer problems, and the answer became the enterprise context graph.

Models get smarter every quarter. They still don't know who your customer is, which invoice is overdue or who owns that ticket. Intelligence without context is just a confident guess. We bring the context.

CRM, ERP, ITSM, databases, mail, documents and chat get connected, normalized and governed once. The result is one context graph, kept in sync in real time, and owned by you, not us.

When off-the-shelf graph databases couldn't keep up, we built our own, and it took years. It loads data 3.7x faster, reads 3.5x more, finds connected data 4.9x faster and stores it 4.7x smaller. Sometimes the long way is the shortcut.

Every token burns compute, and compute burns energy. A typical setup feeds the model whole documents. We send only the connected records each query needs, which means up to 98.7% fewer tokens, and 85% on a typical day. That's cheaper for the CFO and lighter on the planet.

Data is stored 4.7x smaller and connected queries come back in under half a millisecond. A smaller footprint means fewer servers, less storage and less power drawn. Efficiency isn't a feature we bolted on. It's how we're wired.

Not every task needs the biggest model around. Wexa routes each job to the model that fits it: GPT, Claude, Gemini, Mistral or your own. Using a sledgehammer to hang a picture wastes money and megawatts.

Automate a broken process and you get a faster broken process. We mine how work really runs from chats, mail, tickets, logs and SOPs, then design the better version. In one example, 11 steps with 3 rework loops became 6 steps with zero.

Most enterprise data sits in tables that don't know about each other. The context graph does. It discovers how a customer links to an invoice, a ticket, a policy, an owner and a payment, then keeps those links in sync as the business changes. Ask a question and the graph follows those links to fetch exactly what's needed, connected data two hops away in under half a millisecond. Your data stops being a pile of records and starts acting like a map of how your business actually works

Describe the workflow in plain English, in the coding tool you already use. The agent gets built on Wexa and inherits your context and rules. Governance is inherited, not rewritten.

Connect, Map, Ship, Scale. Your first governed pilot goes live within 72 hours. That's long enough to do it properly and short enough that nobody loses interest.

Policies, approvals and the audit trail travel with the context, whatever model, cloud or tool runs it. You write the rules once and they're enforced everywhere. There's no separate rulebook for every agent.

Run it in our cloud, your cloud, your data centre or fully air-gapped. Your keys stay yours, and your data stays exactly where you put it.

We sell software, not data. We don't sell personal information, and we don't use customer data to train third-party AI models. Simple deal.

We're SOC 2 Type I certified and GDPR compliant, with ISO 27001 and HIPAA in progress. Every agent action is checked, logged and kept in a tamper-proof record. Trust is earned one audit trail at a time.

When the AI hype wave hit, we didn't surf it. We built intelligence for these agents that run in production, handle the edge cases and pass security review. Demos are fun, but production is the point.

We say what the product does today and put every number next to the method behind it. If a figure is still being measured, we say so. A customer who feels misled never comes back.

Purchase orders went from 7 days to 2.5, IT tickets from 8 hours to 4, hiring from 44 days to 24 and mining work orders from 10 days to 6. Each is the same process, measured before and after. The end result is what matters.

We build for problems enterprises actually feel, not problems that look good on a slide. If it doesn't make someone's Monday easier, it doesn't ship.

Curiosity sits at the core of the company. We dig into the problem first and write the code second. Measure twice, cut once.

We're a tight crew of about two dozen across engineering, product, growth, DevOps, verification, HR, finance and even hardware. We're small enough to know everyone's coffee order and sharp enough to take on the messiest enterprise architectures out there.

Kiran (CEO) is always thinking about where Wexa goes next. Sneha Jampana is the go-to for all things business.Ravi Sankar (Head of Engg)untangles the client architectures nobody else wants to touch. Our founders each bring more than 20 years of building enterprise systems.

Our founding engineers built the backend from scratch, solo. DevOps scales systems long before they need it, and verification hunts bugs so users never meet them. Everyone here owns something end to end.

We're one team, with each other and with our customers. Titles don't win arguments here. Better ideas do, whoever brings them.

Models change every quarter, but your context shouldn't have to. You can swap providers any time, whether hosted, private or local, and keep the same graph, rules and audit trail.

AI has a growing appetite for electricity, water and hardware, and data centres feel it first. We think the greenest compute is the compute you never run. Wexa sends only the facts each query needs, stores data 4.7x smaller and runs on the infrastructure you already have instead of adding a new stack. Fewer wasted tokens means fewer servers working overtime, and that means a lighter load on the planet

Responsible AI means sensitive fields stay masked, risky actions wait for human approval and every step is accountable. It also means not wasting compute the planet pays for. Innovation and responsibility belong in the same sentence.

Our vision is a world where every enterprise turns its collective knowledge into trusted action. Knowledge flows freely across teams, routine work takes less effort, and AI creates value you can actually measure.

Four letters, one job: give every AI agent the context it's been missing