Forecast-driven decisions
need evidence first.
Tollama Core preprocesses irregular series, runs forecasts, benchmarks models, and routes future requests from evidence. Tollama Trust decides whether those outputs are reliable enough to act on in production workflows.
Teams still stitch together preprocessing, model serving, one-off benchmarks, and trust decisions by hand. There is rarely one clear path from irregular series to an auditable production action.
Every TSFM ships its own install, APIs, and deployment patterns. Serving multiple models means managing multiple stacks with conflicting dependencies.
One-off notebooks with random splits, inconsistent metrics, and no reproducibility. No standardized way to compare models on your data.
Even when a forecast exists, teams often lack a consistent way to score trust, trigger review, or write an audit trail before acting on it.
Tollama Core is the OSS front door for
preprocess -> forecast -> benchmark -> route.
Its first concrete solution is benchmark-backed hourly demand forecasting for operations teams.
Tollama Trust decides whether those outputs are ready for
production action.
Local-first time-series forecasting core for irregular-series preprocessing, unified forecasting, benchmark artifacts, and benchmark-backed routing defaults, with a checked-in concrete-solution path.
Scores whether outputs are reliable enough to act on,
applies policy gates such as allow,
review,
and block,
and writes auditable trust records.
These assets strengthen the Core + Trust story, but they are not the primary product entry points.
The richer benchmark layer behind Core artifacts. Use it when Core needs broader model sweeps, richer reports, and deeper evaluation evidence.
Preprocessing lineage and spline differentiation for irregular-series cleanup, interpolation, and window preparation.
Hero wedge for trust-aware market workflows. Calibration analysis and market signal trust scoring connect Core forecasts to real decision contexts.
The next priority is not more breadth. It is making Core easier to demo, Trust easier to attach, and benchmark evidence easier to operationalize.
Keep the checked-in hourly-demand input, expected-output bundle, and smoke-gated demo path aligned as the Core front door evolves.
Scheduled benchmark runs, regression checks, and release gating for forecast systems.
A smaller, clearer Trust surface that reads as a decision layer on top of Core evidence.
A visible end-to-end demo that connects preprocessing quality, forecast output, trust score, and gate result.
Keep operator summaries, routing rationale, and Trust handoff IDs visible wherever Core artifacts are shown.
Build forecast-driven workflows from benchmark-backed evidence first, then add decision trust where action risk matters.