Skip to content

Roadmap

What exists now, what comes next, and what's deliberately out of scope.

Home

Product roadmap

What exists now, what comes next, and what’s out of scope

Current capabilities

  • ·Seven stage AI lifecycle with shared contracts
  • ·Strategy intake, scoring, and recommended build path
  • ·Data readiness handoff
  • ·Build/RAG lab: retrieval modes (BM25, vector, hybrid, rerank)
  • ·Agent and tool calling mechanics (schemas, boundaries, approvals, misuse evals)
  • ·Training and fine tuning readiness: decision memo, dataset readiness, overfitting and generalization
  • ·Operate / AI Ops / MLOps spine
  • ·Govern live evidence loop and decision engine
  • ·Realize risk adjusted ROI engine
  • ·Model internals explainer (transformers, attention, embeddings, framework placement)

Next technical upgrades

  • ·Real vector retrieval / vector database integration
  • ·Persistent eval run history
  • ·Deeper telemetry integration
  • ·Real tool integrations (APIs, workflow/ticketing engines)
  • ·Real labeling tool plus model registry plus training pipeline integration
  • ·FinOps: cost chargeback and unit economics guardrails
  • ·Operating model: staffing and RACI across the seven stages
  • ·Vendor procurement and third party model risk workflow
  • ·Quarterly benefits tracking (planned vs realized value)

Future production integrations

  • ·External eval stores
  • ·Observability tool integrations
  • ·Model registry integration
  • ·Vector DB adapter
  • ·Role based review workflows
  • ·Exportable governance evidence pack

Intentionally out of scope for now

  • ·Real enterprise data connectors
  • ·User authentication
  • ·Full MLOps platform replacement
  • ·Full model training framework
  • ·Deep PyTorch / TensorFlow notebooks
  • ·Confidential client data
  • ·Cloud infrastructure provisioning

This roadmap keeps the product focused on enterprise AI program delivery rather than turning it into a generic AI course or a full production platform. It intentionally does not implement a transformer or ship training notebooks. The goal is to demonstrate enterprise AI delivery, not to become a deep learning course.

Simulation boundary

This portfolio demo uses deterministic, client side engines and sample data to show enterprise AI delivery mechanics without confidential data or cloud infrastructure. Some signals, including production telemetry and incident and regression history, are modeled. The architecture is built around handoff contracts so real integrations can replace modeled signals later.

Implemented / deterministic

  • ●Lifecycle state & handoff contracts
  • ●Strategy scoring & build path recommendation
  • ●Data readiness derivation
  • ●RAG lab logic (chunking, retrieval, evidence, eval views)
  • ●Operate evidence engine (readiness, lineage, monitoring, regression, incidents)
  • ●Governance decision engine
  • ●Realize ROI engine (leakage, risk discount, payback, NPV)

Modeled / simulated

  • ○Production telemetry
  • ○Real incident history
  • ○Observability-tool integrations
  • ○Real vector database / ANN retrieval
  • ○Real user-feedback stream
  • ○Enterprise data connectors
  • ○Eval run-over-run history