About ZapSight

We ship production AI in 12 weeks.

ZapSight ships production AI to mid-market businesses in 12 weeks. Our AI Production Sprint takes a single use case from commercial validation to a live, deployed system. Two weeks to prove the case in terms a board will accept. Ten weeks to build and integrate it. Each Sprint is scoped against a KPI leadership already tracks, and ends with a system the client owns. Our team is AI-first — people with a deep understanding of data and systems who solve problems creatively, grounded in how a business actually runs. We operate globally across the USA, UK, India, and the Middle East, serving retail, energy, insurance, security, and manufacturing.

Why ZapSight

Sharp business thinking. Deep systems knowledge. Deployment as the finish line.

ZapSight takes AI from a validated use case to a production system in 12 weeks. Sharp business thinking, deep systems knowledge, and a team that treats deployment as the only finish line. That is what ZapSight does. And all that ZapSight does.

How we operate

Values

Ship to production. Start from the business problem, not the technology. Understand the data and the systems well enough to build things that hold the test of time. Invest in a team that is AI-first, because the people behind the solution determine whether it lasts. Move quickly, stay agile, and never mistake speed for shortcuts. Treat the client's KPI as the only success metric.

Ship to production.

Or do not take the engagement.

One KPI, one truth.

The client's metric is the only success metric.

Client-owned at exit.

Leave behind a system the client owns.

100+Reports Automated

3,000+Sites Read

40+Leaders Enabled

The Framework

The ZapSight Operating Model

Read → Sense → Act → Prove

Read

Map the operational signals that already predict cost, delay, leakage, and downtime — from shift logs and machine readings to SAP queues and claims notes — before they become visible to the dashboard.

Shift LogsSAP QueuesPLC SignalsClaims NotesPOS EventsAsset Readings

Sense

Build a live model of where the operation breaks, who feels it first, which signal proves it, and what action should happen before the meeting starts.

Root CausesException PathsDecision PointsControl Limits

Act

Embed the intelligence into the workflow — routing exceptions, recommending decisions, triggering approved steps, and escalating risk at the moment it can still be prevented.

Exception RoutingDecision RecommendationsWorkflow IntegrationAudit Trail

Prove

Tie every deployment to one number the CFO already tracks: downtime, leakage, analyst hours, turnaround, order margin, or incident latency — measured before and after.

Metric DesignBefore/After BaselinesCFO Review EvidenceContinuous Improvement

Founders

Led by Pavan Sathiraju and Murtaza Bootwala.

ZapSight is led by Pavan Sathiraju (McKinsey, Mu Sigma, INSEAD) and Murtaza Bootwala (Amazon, TrueLayer, IIT Bombay, INSEAD) — operators who built the firm to close the gap between AI ambition and production systems.

Co-founder · Product & Engineering

Murtaza Bootwala

Amazon, TrueLayer, PwC · IIT Bombay · INSEAD MBA

Murtaza Bootwala is Co-founder of ZapSight, where he leads product and engineering. Before ZapSight, Murtaza built production systems at Amazon and TrueLayer, and held roles at PwC — delivering at scale before the current AI cycle made it fashionable. He holds a degree from IIT Bombay and an MBA from INSEAD. He started ZapSight to build a different kind of team — AI-first, with a deep fluency in data and systems, invested in building things that last. His role is to make sure every engagement ends with a system that is integrated, monitored, and owned by the client's team. Robust enough to run under real conditions. Reliable enough to still be trusted a year later.

Co-founder · Revenue & Sales

Pavan Sathiraju

McKinsey, Mu Sigma · NIT Rourkela · INSEAD MBA

Pavan Sathiraju is Co-founder of ZapSight, where he leads revenue and strategy. Before ZapSight, Pavan spent formative years at McKinsey and Mu Sigma, where he developed a discipline for making technology decisions that survive boardroom scrutiny. He holds a degree from NIT Rourkela and an MBA from INSEAD. He started ZapSight to build a firm where every AI engagement begins from the business problem — scoped around a KPI leadership already tracks, validated in terms they already use. The creative problem solving that defines ZapSight's approach starts with his insistence that commercial logic comes first.

Methodology

How the system grows from inside the operation

ZapSight starts with the work as it actually happens. Then we build the intelligence around the signals, controls, and decision moments that already shape the result.

READ

Operational read

Capture the systems, queues, documents, handoffs, and informal judgments that already define the work. Map the signals that predict the exception.

1–2 Weeks

SENSE

Signal isolation

Find the small set of signals that predict the exception before humans name it. Model the risk pattern the system must learn to recognise.

1 Week

ACT

Production build

Build the sensing, action, and oversight logic around the workflow it must survive — integrated, role-controlled, and audit-trailed.

6–8 Weeks

PROVE

Proof deployment

Deploy against live exceptions and measure the number leadership already reviews. Every action traceable to the metric that funded the engagement.

1–2 Weeks

Operational Domains

Where the model has to work

Omni-Channel Retail

Conversational commerce, sales enablement, and production-grade visibility.

Manufacturing

Predictive maintenance, quality control, and production optimization.

Construction

Project management, resource planning, and safety compliance.

Furniture & Security

Inventory optimization, customer analytics, and monitoring.

Energy

Asset monitoring, outage prevention, energy variance, and operational efficiency.

Insurance & Finance

Claims processing, risk assessment, and fraud detection.

Trust layer

Read. Sense. Act. Prove.

READ

Operational Signal Mapping

We map the signals a senior operator watches to anticipate performance gaps — sequence patterns, handover transitions, data quality indicators, exception queues, and early rework signals.

SENSE

Production System Design

We structure the decision moments where early intelligence changes outcomes — mapping where the right signal, delivered at the right time, determines the result.

ACT

Production Deployment

We design where the operation should read, route, escalate, recommend, or act — embedding intelligence into existing workflows at the moment it is needed.

PROVE

Proof Engineering

We tie each deployment to one number the CFO already tracks: downtime, leakage, analyst hours, turnaround, order margin, or incident latency.

Bring the operating question your team keeps circling.

Show us the signals, the exception path, and the number that decides whether the work mattered.

Explore our solutions Bring us the hard question