The services market is $4.6T. Software is eating it. Blitzai builds and operates AI that delivers finished work — not tools you configure, not hours you buy. Just results, at software economics, across any industry.
Three eras of enterprise spending. The third is just beginning — and it's 10× the entire SaaS market.
Customer buys labor. Outcomes depend on staffing, availability, and the quality of whoever shows up. Difficult to scale, expensive to run, and impossible to systematise.
Bought: capacity $4.6T Global services marketCustomer buys software access. Customer still does the work — hiring, training, and operating the tools themselves. Better than labor, but the burden of execution stays with the buyer.
Bought: software seats $400B SaaS market (2025)Customer buys finished work. AI plus a small expert team delivers it invisibly. No hiring, no training, no ops burden. Just the result — priced per outcome.
Bought: completed results Same $4.6T Software margins · software scaleSource: Foundation Capital (2024); Fortune Business Insights SaaS Market Report (2025); Bessemer State of AI (2025).
We've heard the sceptical question. Here's the honest answer — drawn from why the first wave of Service-as-Software companies are scaling fast.
Gartner finds 70–85% of enterprise AI POCs never reach production. Building AI agents is easy; operating them — edge cases, monitoring, model drift, hallucinations, compliance — is brutal. Enterprises have tried. They've failed. They're ready to buy.
Senior AI engineer demand has grown 42× since 2023. They cost $400–800K loaded in the US, and you cannot hire 50 of them in 90 days at any price. Enterprises lose the talent war to AI-native startups. Every time. Buying the outcome circumvents it entirely.
If the AI fails, it's the vendor's problem. CFOs and Chief Risk Officers pay enormous premiums to transfer operational liability — SLAs, on-call, and accountability all shift to us. This is why companies rent cloud instead of running data centres.
A $50B-revenue company doesn't care that a vendor costs $500K/year if savings start in month two vs month eighteen. The NPV of time-to-value dwarfs vendor margin in every serious enterprise build-vs-buy analysis.
We are not a point solution. Where a business process currently relies on people or manual tooling, we replace it with an AI-delivered outcome — priced per result, scoped in weeks, not months.
Redlined and risk-flagged contracts delivered in under 30 minutes. Replace 4 in-house lawyers and Ironclad with a per-contract outcome priced at $200–500. Harvey proved the category — we bring it to mid-market firms globally.
AI agents that resolve customer queries end-to-end — not deflection, resolution. $1–5 per resolved ticket vs $4–12 for human agents at 64% blended margin at scale. Decagon built this for Klarna and Notion. We build it for you.
AI SDRs that research, personalise, sequence, and book meetings — autonomously. Replace a 5-person SDR team and ZoomInfo stack with a flat monthly fee. Proven by 11x and Clay; deployable across any B2B vertical.
AI-powered KYC review, fraud signal analysis, and claims adjudication — delivered as finished decisions, not a dashboard to stare at. Reduce human review queues by 80%+ with full audit trails for compliance.
Ambient AI that generates structured clinical documentation from consultations. Reduces physician administrative burden by 3+ hours per day. Delivered as per-note outcomes with HIPAA-compliant single-tenant architecture.
If your business currently pays humans or a BPO to produce a repeatable output — documents, decisions, data, communications, analysis — we can likely deliver it as a priced AI outcome. Start with a strategy call.
Customers won't hand over raw data. They give us something better — operational signal. After 50 customers we've seen 50× more edge cases than any internal team. Customer 51 gets all of it on day one.
Single-tenant deployments (their AWS/Azure, PII masked, zero-training clause). Anonymised learning patterns — which prompts work, which workflows fail. Workflow telemetry: step durations, exception rates, human intervention points. Customers benefit as much as we do.
Tested prompt libraries that handle the vertical's edge cases. Evaluation harnesses that catch regressions before they reach production. These take months to build from scratch — customers on our platform inherit them on day one.
Multi-agent workflows, error recovery, state management. Pre-built connectors to vertical systems — CRMs, billing, compliance, content management. The infrastructure that turns a demo into a production system running 24/7.
These compound silently and cannot be bought. They take 12–18 months to earn. We build them in parallel with delivery — so by the time an enterprise customer asks, the answer is already yes.
You don't get a pyramid of analysts and a slide deck. You get a practitioner who has architected and shipped frontier AI inside Fortune 100 enterprises and VC-backed startups — and who stays to deliver the result.
We tell you which outcome is worth automating, which isn't, and what it would take — grounded in a decade of production AI. No T&M, no padding scope, no upsell. If the numbers don't work, we say so.
Data, training, evaluation, distributed GPU/TPU training, Kubernetes/Databricks deployment, monitoring, and retraining — we own the unglamorous parts that make AI work in production, not just in a demo.
Base fee plus per-outcome pricing in every engagement. If we drift into time-and-materials, we become another services firm. Our incentives are permanently aligned with yours: you only pay when the result is delivered.
Every engagement is led by someone who has personally shipped production AI at TomTom, Deloitte, and as a startup CTO. Wiley-published. Cannes Gold Lion. Visiting faculty at IITs and IIMs. You get the practitioner, not the firm.
The graveyard of Service-as-Software companies is filled with those who chased Fortune 500 as customer #1. We start with mid-market: CEO decides in one meeting, real budget ($100K–500K), hungry for AI to compete with bigger players.
Authoring a deep-learning book for Wiley and building novel CV/LLM architectures means we know what is real versus hype. We can separate the model capability from the marketing — and that call saves customers months and hundreds of thousands.
Credibility built in production, not on slides.
Start with a focused call. We'll tell you honestly what's worth building as a Service-as-Software outcome, what isn't — and what it would take to ship it in 60 days.
umang@blitzai.in · India & US · Outcome-based engagements across industries