EXECUTIVE SUMMARY
Execution infrastructure for AI-native organizations.
A one-page synthesis of TAB@Work — the thesis, the product, the market, the model, and the ask — aligned with the investor deck.
01 — THE OPPORTUNITY
AI is transforming work. Work isn't getting executed.
SaaS gave every team its own tool. It never gave the organization a way to execute across them. As AI multiplies what each tool can do, the seams between them widen — and the cost of dropped handoffs compounds. The next bottleneck is not intelligence. It is the infrastructure to act on it.
Every team has a tool. No one has the workflow.
Work crosses teams, systems, and agents. No tool owns the handoff — so the handoff is where work dies.
Dashboards observe. They don’t recover.
Visibility without recovery is just a faster way to watch work fail.
Automation fixes the repeatable. The rest still stalls.
The work that stalls is the unplanned, the ambiguous, the cross-system — the work no automation was built for.
02 — THE PRODUCT
One execution layer across the organization.
TAB is a horizontal coordination and recovery layer — sitting alongside leadership, managers, employees, enterprise systems, and AI agents without replacing any of them. It observes in context, detects the exact breakdown, and triggers recovery inside the environments work already happens in.
FIXED CORE
Core Engine
- Observation logic
- Breakdown detection
- Recovery orchestration
- Continuity model
ADAPTIVE WRAPPERS
The same core engine is expressed through adaptive wrappers tuned to each team’s context — tools, language, permissions, and rhythm. The infrastructure stays constant; the operating layer adapts.
Customer Operations · AI Transformation · IT & Governance · High-friction teams — same engine, different operational expression.
03 — THE LOOP
A closed loop, not a dashboard.
The execution loop: observe, detect, recover, learn. Each recovery trains the layer; recurring breakdowns surface as structural patterns, not individual signals.
Observe context
Read the operational context where work already happens — tools, roles, handoffs, status.
Detect the exact breakdown
Identify the specific point where execution has stalled — not a score, an event.
Trigger the lowest-friction recovery
Route the smallest action that unblocks work to the right actor, in-flow.
Return work with continuity
Resume with context, status, and next step preserved across systems.
04 — WHY NOW
Every company is becoming AI-native. Most are not execution-ready.
SaaS tools were built to optimize work inside each team. None were built to recover the work that falls between them. As AI multiplies what each tool can do, the seams widen — and the cost of dropped handoffs compounds.
Stanford/MIT trial of 5,179 contact-center agents (Brynjolfsson et al.).
Gallup 2026.
Gartner: enterprise adoption 15% → 50% by 2027.
05 — WHY NOT EXISTING TOOLS
Existing tools weren't built for the seams.
Automation moves data. Assistants generate inside a conversation. Monitoring watches people. None of them recover stalled work.
Automation platforms
Workato · Zapier · n8n
Move work on a trigger. They don’t detect when execution has stalled, and can’t recover a handoff that fell through the seams.
AI assistants
Copilot · ChatGPT · Claude
Generate inside a conversation. They don’t hold the organizational context — permissions, roles, handoffs — where work disappears.
Employee monitoring
Productivity surveillance
Watch people to measure effort. TAB measures friction events, not people — aggregate and anonymous by design.
06 — TRACTION & VALIDATION
Validation where execution breaks first.
Early-stage validation across a live design partnership, field research, and behavior-first prototypes. Pilot KPIs are being formalized with design partners.
US design partnership
Embedded in a trading workflow, validating execution recovery in a high-stakes, high-friction environment.
Call-center field research
Focused on extreme early attrition and the operational friction that drives it — where execution breaks first.
Behavior-first AI prototypes
Testing intervention timing, trust, and low-friction recovery — proving the loop can act without surveillance.
OPERATIONAL METRICS
07 — BUSINESS MODEL
Land on a broken workflow. Expand across the organization.
TAB enters through a single broken workflow — the place execution stalls most visibly — then expands across adjacent handoffs and teams. The model is enterprise SaaS: per-environment licensing that scales with the surface area TAB recovers.
PILOT
Land on a broken workflow
Enter through a single workflow where execution stalls most visibly. Design partnerships validate detection and recovery in a live environment — no fee, no rip-and-replace.
EMBED
Per-environment annual license
Once the loop is proven, TAB expands to adjacent handoffs within the same organization. Pricing scales with the surface area TAB recovers — per environment, not per seat.
SCALE
Multi-environment platform
Repeatable deployment across functions turns pilot evidence into a category. The same core engine, expressed through adaptive wrappers, lands in new environments with minimal configuration.
08 — ROADMAP
From pilot to category.
NOW
Pilot
Embedded pilots with 3–5 enterprise design partners across customer operations and AI transformation.
NEXT 6 MONTHS
Embed
Deepen integrations. Move from detection toward autonomous recovery inside approved guardrails.
12 MONTHS
Scale
Repeatable deployment across functions. Turn pilot evidence into a category — execution infrastructure for AI-native organizations.
09 — THE ASK
Build the execution layer with us.
We're looking for the operators and investors who see the same bottleneck.
10 — FOUNDER
Built by a systems architect focused on the gap between knowing and doing.
Danya Dotan combines behavioral science, systems thinking, and AI product design. Her work focuses on execution recovery, operational continuity, and human–AI collaboration — the infrastructure organizations need to adopt AI through trust rather than surveillance.
Danya Dotan · Founder, TAB@Work · Tel Aviv, Israel
11 — WHAT MUST BE RIGHT
Four points worth getting right before sending.
The synthesis holds, but four points deserve to be stated explicitly before this goes out — a measurable win, the honest answer to the obvious risk, anonymity as a sales advantage, and a clean separation between the two pipelines.
A single number defines success
Pilot success is measured in one number: the share of stalls the system recovers autonomously, without human intervention, with detection under five minutes. The first 90-day target: 70% autonomous recovery across three defined friction scenarios (IT governance, customer ops, data workflows). A verifiable metric — not "adoption" or "satisfaction", but direct measurement of TAB's ability to return work that would otherwise fall between the tools.
The real risk — and the answer to it
The expected question: why wouldn't Slack, Asana, or ServiceNow add this themselves? The answer: they can add an alert, but not recovery. Recovery requires reading cross-tool, in real time, from all of the customer's systems, and acting across their own boundaries. That is a structural conflict: the big tool cannot be the judge in its own trial. TAB does not compete with existing tools — it sits beneath them, as a coordination layer none of them can or wants to be.
Anonymity as a sales shield
TAB's anonymity is not only an ethical value — it is a market-entry engine. A "coordination" tool usually meets organizational resistance rooted in fear of employee surveillance. TAB enters faster because it threatens no employee: it measures friction events, not people, and anonymity is an architectural property, not a promise. The ethics, in this case, become an accelerator of the deal rather than a cost.
Keep the pipelines separate
Leads must be classified precisely between two types: Design Partner (design partner — goal: product validation and co-development) and Investor (goal: return and measured risk). The follow-up conversation, the message, and the terms differ fundamentally between the two. Mixing them in the pipeline creates communication friction and carries a high cost at a late stage — exactly what TAB itself exists to prevent.