Findola Platform · Model flywheel
The flywheel that turns every Findola interaction into better, owned models.
Lumind is Findola's data, evaluation, and fine-tuning flywheel. It captures production traces, synthesizes privacy-safe training data, runs evals, and continuously fine-tunes the retrieval, reranker, verifier, and agent models that power Cortexa, Retrova, and Agentiq. It is how Findola's quality compounds each month and how the data moat becomes real model IP.
Why Lumind exists.
AI search and agent quality silently decays and varies per corpus. Teams have no systematic way to convert usage into model improvements without exposing customer data. Manual eval and fine-tuning do not scale, and generic models never specialize to a customer's domain.
What it does
Lumind ingests anonymized interaction signals (queries, retrievals, verifier verdicts, human overrides, outcomes), curates and synthesizes datasets, generates hard negatives and eval sets, and orchestrates NeMo fine-tuning plus DPO/RLHF on GPU clusters. It governs what data is used with per-tenant isolation and opt-in, measures every model against golden datasets, and promotes improved models into NIM serving, closing the loop.
What ships in Lumind.
Every capability below maps to a real workload that needs accelerated compute.
Governed trace capture
Anonymization and per-tenant governance built in.
Synthetic data
Hard-negative and training-set generation from production.
Golden-dataset evals
Drift and regression detection through shadow evals.
Fine-tune orchestration
NeMo fine-tuning and DPO/RLHF, with a model registry and promotion.
Private fine-tunes
Your data, your model, for the enterprise tier.
Architecture.
Findola and Agentiq traces are anonymized, curated with RAPIDS, and turned into synthetic data and eval sets. NeMo fine-tunes or runs DPO on DGX, models are checked against golden sets, and the best are promoted to NIM for Retrova, Agentiq, and Cortexa. Governance and lineage run throughout for the EU AI Act and audit.
NVIDIA hardware
- DGX / H100 / H200. Fine-tuning and DPO/RLHF, infeasible on CPU at scale.
- A100. Eval and batch inference for dataset labeling.
- RTX workstations. Dataset and eval development.
NVIDIA SDKs & libraries
- NeMo (Customizer/Curator/Evaluator). The full model lifecycle for owned, governed model IP.
- RAPIDS. GPU data curation of TB-scale trace sets.
- TensorRT-LLM. Graders and synthetic-data generation at high volume, cheaply.
NVIDIA software
- NVIDIA AI Enterprise. A supported training and serving platform for enterprise and sovereign tiers.
- NIM. Promote improved models to serving, closing the loop into production.
Lumind is GPU-essential.
Fine-tuning, DPO/RLHF, synthetic-data generation, and TB-scale curation are all GPU-bound. Continuous improvement at Findola's data volume is only economical on DGX and H100 with RAPIDS. CPU-only cannot keep the flywheel turning.
The moat.
The flywheel is the deepest moat: more usage, better data, better models, better product, more usage. Per-customer private fine-tunes raise switching costs further.
Lumind shows a genuine, compounding data flywheel plus owned model training on NVIDIA's full NeMo, DGX, and RAPIDS stack. Those are the data-moat and AI-depth signals reviewers weight heavily.
Ecosystem link: Lumind is the training engine. It trains the models that run Cortexa, Retrova, Agentiq, and Vaulta.
See Lumind on your own data.
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