A senior data scientist is hard to hire and harder to keep. In a 4-person team, two seats sit open at any time and the senior churns inside 18 months.
Training, serving, and governing models at scale needs a platform of its own — a dedicated engineering team, roughly $500K, and 12 to 18 months. The wrong scale for a 3-to-8 scientist shop.
BoT, OIC, PDPA. Model cards, audit trails, and approval gates aren't optional anymore — they are the cost of shipping a model at all, not something you bolt on later.
Each agent has a role, a deliverable, and a quality bar — like a real team, except they don't sleep, don't churn, and write up their work in your team's language.
First-look EDA on any new dataset: distributions, missingness, outliers, correlations, candidate targets, data-quality flags.
Proposes candidate features — aggregations, lags, ratios, interactions. Tests each one's contribution and keeps the winners.
Trains and tunes across sklearn, XGBoost, LightGBM. Walk-forward CV, leakage checks, and mandatory baselines — never a leaderboard without a floor to beat.
SHAP, feature importances, partial dependence. A plain-language narrative on why the model decides what it decides. In your team's language, on demand.
Evaluates every upstream report against a tunable heuristic catalog and routes it: auto-approve, escalate to a human, or block. The gatekeeper that lets a non-technical supervisor sign off.
And an Orchestrator runs the team — sequencing the work, replanning when a model underperforms, and escalating to a human when judgment is needed. You manage a team. You don't operate a tool.
A new dataset lands — here, customer churn. Every step runs on its own; you only review and approve. It's just one of many problems the same team handles:
50K rows · 47 columns. 12 numeric, 28 categorical, 4 datetime, 3 text. 8% missing concentrated in 5 columns. 3 likely ID columns. Two candidate targets surfaced.
The local LLM identifies the domain ("banking customer data") and proposes 25 candidate features. The deterministic engine tests each. 14 keepers retained on contribution.
Five baselines first. XGBoost wins with AUC 0.81 [0.78, 0.84]. It adapts: "tree models are winning — focus the remaining search there." No deep learning attempts on 50K rows.
SHAP analysis. Top drivers: days_since_last_transaction, product_count, balance_volatility. Narrative drafted in your team's language. Model card generated with population, limits, and retrain date.
"Worked on the retail-churn dataset. AUC 0.81 with high interpretability. Recommend validating the temporal split with the business owner. Top driver is engagement decline, not pricing — counter to the prior hypothesis."
That's the whole run, end to end — not a demo. A team of agents doing the unglamorous work, repeatably, is what earns trust in the first month — not a wow moment in the first hour.
The mistakes that make a model look great in dev and fail in week four — baseline-blindness, CV leakage, survivorship, look-ahead — are refused at the framework level, not left to discipline.
0.85 R² looks great until naive AR(1) hits 0.85 too. Every model must beat the floors — or it doesn't ship.
Random K-fold leaks the future into training. The framework refuses it for time-series data — before training starts.
A model that's accurate today gets worse as the world changes. We watch the live data and flag when it's time to retrain — before accuracy quietly slips.
Each model ships with a card: what it predicts, on which population, with which known limitations and survivorship caveats. Governance from day one.
The local LLM is the translator. The frontier model never sees a row — only the abstracted problem.
Reads schema and distribution shapes. Builds an abstracted problem. No rows leave.
Reasons over the schema and framing — generates code, strategy, interpretation. Never sees the data.
Validates against your real columns, executes in a sandbox, returns the result.
You choose per workspace — and a PII guardrail enforces what may cross the boundary either way.
Operate the team through the portal — watch every step and approve, pause, or reject each one — or drive the same process over MCP. Underneath, a calculation engine trains the models and a repository tracks every run, so every prediction is reproducible and audited.
Human-in-the-loop by design: the Reviewer pre-digests every result, and a person approves, pauses, or kills each step before anything publishes. Nothing reaches production without a sign-off — and every run stays reproducible and audited.
It runs where your data already lives. The LLM is your call — local by default, frontier when it matters, your API key and your bill.
Your cloud or on-premise — AWS, GCP, Azure, IBM Cloud, INET, or bare metal. Full control over data and models, and we train your team to operate and scale it.
We host, train, and serve your models for you — predictable monthly cost, auto-scaling and updates, first models in days not months.
The platform is the easy part; a successful deployment is people. Infozense engineers — the ones who build and operate it, not slide-deck consultants — work alongside your team for the first 8–16 weeks: data onboarding, feature and model build, reviewer-threshold tuning, operator training. Knowledge-transfer first: your team owns the system at the end.
