Process proof
TaskCap starts with the worker's bottleneck, not the AI tool.
The engine is designed to avoid generic AI advice. It listens for the actual source of friction, structures the evidence, and recommends only tools that plausibly match the primary problem.
Diagnostic pipeline
Six stages from transcript to recommendation.
Structured employee interview
TaskCap asks what the worker does, where the task slows down, which systems are involved, how often it happens, and what a successful output looks like.
Task and bottleneck extraction
The interview becomes structured records: task name, specific bottleneck, friction quote, systems involved, frequency, time cost, desired output, and priority.
Capability classification
Each task is classified by whether current AI can help, where human judgment remains necessary, and whether the task is blocked by policy or real-world constraints.
Verified tool retrieval
The system retrieves candidate tools from TaskCap's catalog by capability, role, workflow language, systems involved, and specialist fit.
Fit-aware reranking
Candidate tools are ranked for the primary bottleneck. Generic tools are penalized when a specialist exists, and adjacent tools are rejected when they solve the wrong task.
Worker and consultant reports
Employees receive one practical recommendation. Consultants receive the aggregate diagnostic: readiness, bottlenecks, opportunities, honest gaps, and a roadmap.
Why it is different
TaskCap gives consultants a repeatable diagnostic system.
A consultant can still use judgment, workshops, and client context. TaskCap supplies the structured evidence layer underneath that advice.
Honesty constraint
No fake magic.
TaskCap's strongest recommendation is sometimes not to buy a specialty AI tool. If a worker's bottleneck is too high-judgment, policy-bound, physically constrained, or poorly served by the current market, the report says that directly and gives a practical manual workaround.
Open the sample diagnostic