Fit team behavior regime HMM

Learn persistent privacy-safe team operating regimes and transitions with a Gaussian hidden Markov model.

Markov & state-space control Estimate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Teams operate in persistent modes, not on a smooth trend. This identifies those regimes and the transitions between them from privacy-safe aggregate signals.

Evolves static developer typologies into longitudinal team-state dynamics, dwell times, and regime changes.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field	Type	Role	Required
feature_names	array of string ≥ 1 item	Evidence	Yes
max_iter	integer ≥ 2	Your calibration	Optional
min_unit_size	integer ≥ 2	Your calibration	Optional
n_states	integer ≥ 2	Your calibration	Optional
seed	integer	Numerical control	Optional
sequences	array of objects (3 fields)	Evidence	Yes
tolerance	number > 0	Your calibration	Optional

Each `sequences` record

Field	Type	Required
observations	array of objects (0 fields) (≥ 3 items)	Yes
unit_id	string (non-empty)	Yes
unit_size	integer (≥ 0)	Yes

Example input

{
  "feature_names": [
    "flow",
    "reliability"
  ],
  "max_iter": 30,
  "n_states": 2,
  "seed": 3,
  "sequences": [
    {
      "observations": [
        {
          "flow": -2,
          "reliability": -1.8
        },
        {
          "flow": -2.1,
          "reliability": -2
        },
        {
          "flow": -1.9,
          "reliability": -2.1
        },
        {
          "flow": 2,
          "reliability": 1.9
        },
        {
          "flow": 2.1,
          "reliability": 2
        },
        {
          "flow": 1.9,
          "reliability": 2.1
        }
      ],
      "unit_id": "platform",
      "unit_size": 8
    }
  ]
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

Example output

{
  "fit": {
    "converged": true,
    "features": [
      "flow",
      "reliability"
    ],
    "iterations": 4,
    "log_likelihood": 9.682,
    "observations": 6
  },
  "interpretation": "Latent states describe team-level behavioral regimes, not employee quality or intent.",
  "method": "gaussian_behavior_hmm_v1",
  "normalized_entropy_rate": 0.344,
  "privacy": {
    "min_unit_size": 5,
    "suppressed_sequence_count": 0
  },
  "sequences": [
    {
      "current_state": 1,
      "regime_change_indexes": [
        3
      ],
      "state_path": [
        0,
        0,
        0,
        1,
        1,
        1
      ],
      "unit_id": "platform"
    }
  ],
  "states": [
    {
      "centroid": {
        "flow": -2,
        "reliability": -1.9667
      },
      "expected_duration_periods": 2.91,
      "label": "low_flow",
      "state": 0,

Truncated for display — the full payload is 68 lines.

How it works

Markov & state-space control — Choose a policy over evolving states when today's action changes tomorrow's options.

1 Learn persistent privacy-safe team operating regimes and transitions with a Gaussian hidden Markov model.
2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Assumptions & guardrails

States are decision-sufficient at the chosen cadence and transition evidence is recent, identifiable, and not silently pooled across incompatible regimes.
A policy is conditional on the declared state abstraction and transition model; hidden omitted state can invalidate its ranking.

Minimum evidence

sequences: required and organization-defined
feature_names: at least 1 rows/items

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

metric/outcome definitions, entity grain, observation window, costs, thresholds, priors, and constraints represented by the function inputs

Calibration workflow

1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "learn persistent privacysafe team operating regimes" }
  → finds "fit_team_behavior_regime_hmm"

gitrevio_capability_describe
  { "capability_id": "fit_team_behavior_regime_hmm" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "fit_team_behavior_regime_hmm", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

MCMC project completion forecast

Forecast live task and project completion with a censored Bayesian lognormal model, MCMC uncertainty, dependencies, and finite parallelism.

Sequential Bayesian & bandits

Optimize workforce policy tree

Optimize staged team/role capacity actions through uncertain demand by Monte Carlo backward induction.

Constrained optimization

Simulate contextual thompson bandit

Simulate Bayesian contextual Thompson sampling and quantify intervention reward, regret, and policy uncertainty.

Sequential Bayesian & bandits

Analyze coordination entropy

Quantify privacy-safe cross-team seam complexity, concentration, latency, and failure load.

Decision analysis

Analyze delayed management feedback stability

Stress the dynamic stability of a delayed signed organizational feedback model: build a VAR companion matrix from interval-valued lagged influences, evaluate midpoint, interval corners, and sampled simultaneous coefficients, calculate spectral and transient amplification margins, and rank one-edge damping leverage without claiming an exhaustive robust-control certificate.