lab0.ai

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Enterprise SaaSdeployed in months weeks

Lab0 builds AI agents that turn enterprise software from a consulting-heavy deployment into a self-serve product. Compress implementation timelines from 3-12 months to weeks.

IIT Bombay · IIT Madras · Ex-Founders · Ex-Emergent

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Enterprise software is sold like a product but deployed like a consulting project.

The delays are predictable: customer context is scattered, delivery teams rebuild the same artifacts, and every decision gets rediscovered across meetings.

3–12 mo
Time to go live
Systems like ERP, ITSM, identity, security, and data platforms take months to deploy after contracts are signed.
80%
Repetitive work
Implementation teams repeatedly perform the same process for each new customer, even though patterns are similar across deployments.
Manual
Fragmented process
Understanding environments, mapping processes, designing policies, planning integrations, migrating data—all done manually across scattered channels.
40–50%
Customer-side delay
Approvals, missing data, firewall changes, and shared internal bandwidth stall the work after the deal is signed.
$70–80k
Services drag
Customers routinely pay large services costs on top of license spend before they see production value.
4+
Handoff chain
Discovery, architecture, implementation, validation, and customer teams each carry partial context.
01Vendors carry large, expensive service teams
02Customers wait months to realize value
03Revenue locked behind long onboarding cycles
04Slow rollouts, hidden costs, unpredictable outcomes

Productize the implementation itself.

Instead of hiring more implementation specialists, ship a standardized AI-assisted workflow that compresses deployment from months to weeks.

01

Async Context Gathering

Agents gather context across communication channels—Slack, email, docs—without blocking your team.

$lab0 gather --channels slack,email,confluence
02

Auto-Configure & Deploy

Generate deployment plans, configure integrations, design policies, and customize workflows automatically.

$lab0 configure --integrations sso,hris,ticketing
03

Learn & Improve

Each deployment improves the system. The agent learns from previous rollouts and optimizes future implementations.

$lab0 optimize --from-history --encode-best-practices

Field notes from enterprise implementation.

Short operating notes on why deployments stall, how teams keep context alive, and where agents can take repeatable work off the critical path.

01

Why the signed contract is not the start line

The deployment clock starts before procurement finishes. The handoff only works when sales, security, data owners, and implementation agree on what will be true on day one.

02

The hidden cost of customer-side blockers

Most implementation drag comes from missing access, unresolved ownership, and data that does not match the target system. Agents can surface those blockers before the kickoff call.

03

A deployment runbook should learn every time

Every rollout produces decisions, exceptions, and artifacts. The next customer should not pay for that knowledge to be rediscovered from scratch.

Let’s talk.

We’re building AI agents that make enterprise software truly self-serve. If your implementation timelines are measured in months, we should talk.

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founders@lab0.ai
Lakshya Gupta
Sujay Srivastava
Onkar Borade