NinjaTech is now an Infosys portfolio company. We have a one-year exclusive window to land joint pilots, prove the deployment model, and turn the 300K-strong Infosys delivery base into the largest enterprise distribution engine for AI Employees on the planet.
Two facts reshape every pipeline and roadmap conversation we have for the next twelve months.
The partnership is signed and in motion. Infosys is not a channel, not a reseller, not an integrator we tolerate. They sit on the cap table. Their delivery teams, their account executives, their Topaz fabric, all carry incentives aligned to our growth.
Twelve months to land joint pilots, publish reference customers, and design how we co-sell the AI Workforce and deliver services jointly. After that window, competitive solutions enter the same accounts. Speed of pipeline build is the single most important metric this year.
Modernization, an app-meta layer, central orchestration, vertical agents, auditing, and continuous knowledge surveillance. These map directly to existing Infosys engagement types, which is why they matter.
Taking existing client applications and rebuilding them with modern stacks, or designing net-new applications from scratch for those same organizations.
Build a meta-application whose specific job is to modernize old, clunky enterprise applications, turning them into faster, more usable, AI-augmented products without a full rewrite.
Have SuperNinja act as the central orchestration layer, managing all other applications and agents from one place so users get a single front door into the workforce.
Each agent tuned to a particular enterprise workflow, rather than relying on one general-purpose agent to handle every job. Depth beats generality at the buyer level.
An agent that continuously scans customer data lakes, contracts, or compliance artifacts for anomalies, drift, or risk flags. Particularly valuable in BFSI, insurance, and healthcare where regulators require provable audit trails.
Agents that run perpetual surveillance on a specific domain (competitive pricing, supplier inventory, regulatory filings) and surface deltas to the user only when a threshold is crossed, instead of dumping raw feeds.
Deepak laid out four ways to take Ninja to Infosys customers. Armand ranked them by ease of landing; Deepak endorsed the order ("I like your ranking"). The agreement: phase it across ~90 days, start with the orchestration layer, don't boil the ocean.
Priority / lowest-hanging fruit. A middleware agent layer that holds full conversation context and calls multiple systems and agents (RAG, SAP, Salesforce, Azure) to answer a customer end-to-end. Deepak's "holy grail" — there is real agent sprawl, 2–3 live RFPs, and Topaz has only a weak play here. Positioned as the alternative to Salesforce Agentforce.
Tied second. Retire / "agentify" legacy apps, move them to cloud, and build small fit-for-purpose apps (fraud management, warranty management for manufacturing) — spec-based coding with a real database and workflows, not throwaway "vibe coding."
Tied second. Salesforce has ~170K customers and many are frustrated (clunky, costly); today their only alternative is Dynamics. Lift apps off Salesforce one at a time over ~2 years — e.g. Encino (a clunky 20-screen lending app) rebuilt as a custom app. Open question: how to manage Salesforce's ongoing feature releases afterward.
Highest effort. Arrive at a customer with a ready vertical suite (contact center for payers, prior-auth, patient / customer onboarding + KYC). Needs the most build and customer feedback — the platform stitches components together with human-in-the-loop, it doesn't have to ship everything pre-built.
These aren't hypotheticals — Suyash walked the team through warm, in-flight opportunities where Ninja can land now, plus a set already brewing. "Use cases are what's gonna land us."
Contact-center build on AWS Connect needing case management + a knowledge base; they were defaulting to Salesforce. Suyash holds the full architecture and is pushing it with Bal & Dushyant. The warm anchor account.
ServiceNow + Salesforce + Zuora on a Snowflake data lake. 70% of pricing is custom (only 30% on the price sheet) at a $4B-revenue company, done today by manual spreadsheet dumps. Ninja = the pricing/reasoning engine + the orchestration layer.
Loyalty currently on Siebel; they want an AI-native loyalty platform and find Salesforce Loyalty Cloud too pricey and too new. A differentiated travel & hospitality showcase.
Onboarding use case (Suresh-driven), with the relevant context already shared. Sits squarely in the modernization + orchestration sweet spot.
Avnet, Teladoc, Molina, and AT&T (AT&T willing to take a product demo). Plus a banking fraud use case that is already built (no rebuild) and contact center as a cross-industry play with AWS support.
Arash's delivery model, and the heart of the co-sell: an FDE is "as critical as the AI employee." Build a POC in 24 hours, iterate live with the customer, then clone the working agent across accounts. The customer doesn't even have to live in Slack — Infosys / Simplus can run it for them and bill the hours.
Each play is built so an Infosys consultant can run it without back-office support from us, and so the pricing slots into the customer's existing procurement path. Pilots over POCs. Headcount-cost over seat-licenses. Verticals over breadth.
Bundle a NinjaTech agent into a 30-day pilot inside an existing Infosys Topaz engagement. Pricing rolls into the Topaz SOW so the customer's procurement path stays unchanged, and time-to-value is measured in days, not quarters.
Ship a packaged offering tied to specific named roles (AI Customer Support Specialist, AI Procurement Analyst, AI Loan Underwriter Assistant). Sell against the customer's existing headcount lines on their general ledger, not against software seat licenses.
Pick two to three verticals where Infosys is deepest (BFSI, Insurance, Healthcare Payers) and ship vertical-specific agent packs first. Depth in three verticals beats breadth in thirty, and gives Infosys delivery teams reusable playbooks instead of bespoke builds.
Train the Infosys consultant base on a small set of canonical NinjaTech use-cases so every consultant already on a customer site becomes a potential deal trigger. Bonus: consultants get AI tooling they actually use day-to-day, which is the strongest possible reference selling motion.
Turn the warmest existing Topaz customer (e.g. Citizens Financial Bengaluru Hub) into the public reference case study, and open the next five BFSI accounts off that single win. Reference quality beats reference quantity at this stage.
Tie pricing to fully-loaded headcount cost, not seats. The ROI story that lands with COOs is one AI agent at a fraction of the cost of a junior analyst, working 24/7, with no attrition risk. The unit economics map cleanly to their existing budgeting model.
Lean into VPC and on-prem single-tenant for regulated verticals. Consumer-grade assistants (Copilot, Gemini Enterprise, ChatGPT Enterprise) cannot meet BFSI, healthcare, or government compliance bars. That is the structural moat, and it should anchor every regulated-vertical pitch.
Consumer-grade assistants from the hyperscalers cannot meet the regulatory posture demanded by BFSI, healthcare, government, and defense. That gap is the structural moat. We should anchor every regulated-vertical conversation on it.
Four layers, top to bottom. The hyperscalers cannot ship the top two without rebuilding their entire control plane. We ship them today. Every BFSI, healthcare, and government conversation should start at the top of this ladder and refuse to come down.
Three lenses. The buyer inside the customer organization, the account that fits the joint motion, and the anti-pattern we deprioritize in year one to keep velocity up.
Every deal we want to close in year one runs this exact path. Five steps, thirty days each at most, no exceptions, no detours.