lab0 product overview

An AI implementation workspace for delivery teams.

Capture context. Structure requirements. Find gaps. Generate artifacts. Configure the work.

FEATURE TOUR 2026 lab0.ai
01 / The problem

Implementation teams lose time because the truth is scattered.

Calls, documents, emails, chat threads, exports, and system screenshots all hold part of the answer. Delivery teams still have to assemble it by hand.

Context is everywhere

Requirements live across meetings, documents, inboxes, channels, and customer systems.

Gaps show up late

Unknowns appear during build, QA, or migration because discovery never became a usable checklist.

Artifacts drift

BRDs, mappings, and runbooks become detached from the evidence that justified them.

Config work repeats

The same account context gets rebuilt for every agent, engineer, and handoff.

02 / What Lab0 does

Lab0 turns scattered implementation context into work a delivery team can ship from.

It keeps discovery, files, evidence, questions, artifacts, process maps, and agent runs in one workspace.

03 / The implementation loop

From raw context to configured systems, in one workspace.

01

Capture

Pull in calls, docs, emails, channels, and uploaded files.

02

Structure

Compress raw context into questions, requirements, and evidence.

03

Find gaps

Separate what is known from what still needs an answer.

04

Visualize

Map systems, actors, handoffs, and data flow.

05

Generate

Create BRDs, mapping specs, runbooks, and QA artifacts.

06

Configure

Use the agent and config plane to do the implementation work.

04 / Context ingestion

Lab0 starts where implementation context already lives.

Upload what you have, connect what the team uses, then let Lab0 compress the raw material into implementation-ready knowledge.

Files
Files
docs, decks, sheets
Google Drive
Google Drive
shared folders
Outlook
Outlook
email threads
Slack
Slack
delivery channels
Microsoft Teams
Teams
client channels
Zoom
Zoom
live calls
Fireflies
Fireflies
call notes
Granola
Granola
meeting notes
05 / Discovery becomes answer management

A central place to know what is answered and what still blocks implementation.

Lab0 auto-fills the discovery questionnaire from known context, then turns the missing pieces into focused questions.

Already known

  • Requirements found in files and transcripts.
  • System names, owners, and handoffs.
  • Fields, mappings, constraints, and approvals.
  • Evidence attached to each answer.

Still needed

  • Open implementation decisions.
  • Missing field definitions and edge cases.
  • Unknown source of truth or owner.
  • Questions to ask on the next call.
06 / AI interviews

When the client is offline, Lab0 sends the questionnaire for them to fill.

AI interviews turn missing discovery answers into a structured client-facing questionnaire.

  • Send focused questions to the right client stakeholders.
  • Collect answers asynchronously without another meeting.
  • Route completed responses back into the implementation workspace.
AI interview setup
07 / superdiscovery

During calls, Lab0 helps the team ask the questions that matter.

It follows the live conversation, updates the discovery state, and surfaces pending questions while the stakeholders are still in the room.

  • Analyze live calls and transcripts as implementation evidence.
  • Generate follow-up questions from the gaps in the workspace.
  • Keep answers tied to source context.
SuperDiscovery live transcript

Process visualizer shows their systems and how data flows

08 / story
process visualizer

Built from superdiscovery context. Teams can see actors, systems, handoffs, data flow, and the gaps that need attention before config work starts.

09 / Lab0 agent

The agent turns the workspace into implementation artifacts.

BRDs, mapping specs, migration runbooks, QA plans, and handoff docs are generated from the same evidence base.

  • Short operator-style answers for delivery teams.
  • Tool traces and run history so the work is reviewable.
  • Generated outputs land back in the workspace.
Lab0 agent

Artifacts BRDs, mapping specs, migration runbooks, and delivery outputs

10 / story
Artifacts

Every output stays attached to the files, calls, answers, and decisions that produced it.

Config plane agent-assisted implementation with you in the loop

11 / story
config workspace

The agent uses the workspace brief to propose implementation steps, show the work, and wait for approval before anything important changes.

12 / What delivery teams get

Less reconstruction. More implementation.

Lab0 makes the implementation state visible, answerable, and actionable.

context
One workspace briefCalls, files, channels, and evidence stay together
discovery
Known vs missingOpen questions become the next best actions
process
Visible data flowSystems, actors, and handoffs are mapped
artifacts
Evidence-backed outputsBRDs, specs, and runbooks trace to source context
agent
Scoped operatorThe agent works from the same account state
config
Work you can verifyPlans, changes, and runs stay reviewable
handoff
No re-briefingThe next person starts from the workspace
delivery
Fewer late surprisesGaps surface before the work gets stuck
lab0 lab0.ai

Implementation teams should not rebuild the same context every week.

Book a demo founders@lab0.ai
END FEATURE TOUR lab0.ai