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Strategy Stability Pilot

Any tool can cite evidence and still change strategy unpredictably. Atlas governs strategy evolution over time using drift attribution, stability constraints, and delta-based justifications, so "learning" doesn't look like noise.

Now→March: public-data stability report

March: 4–6 week pilot

Focus: consistent strategy evolution

Output: versioned strategy + diffs + drift reports

Measured: driver churn ↓ with delta-justified changes

Consistency is the Bottleneck

Citations are not enough when the system can justify different changes each quarter

Your internal tools already produce reasoning and citations for policy changes.

Yet quarter-to-quarter, the "reasons" rotate: Q1 uses one signal, Q2 adds another, Q3 changes again—so the team must re-verify every suggestion.

The issue is not context windows or access to data. The issue is unstable decision logic: "learning" looks like randomness.

Dezerv needs explainable deviation under constraints: strategy evolution that stays coherent unless evidence truly forces a change.

Current Bottleneck

Snapshot-based tools can produce persuasive narratives, but they do not control variance of reasoning over time.

Atlas Solution

Atlas makes strategy a governed, versioned object: drift is diagnosed, changes are constrained, and justifications must cite delta evidence (what changed).

Security & Data Governance

Strategy materials and preference profiles must not be used to train any model. Data handling must support enterprise controls and configurable retention.

Bedrock

AWS Bedrock

Vertex AI

Google Cloud Vertex AI

MS+OpenAI

Microsoft Azure OpenAI

All providers offer:

Opt-out from training foundation models
Isolation between customers
Enterprise logging/retention controls (configurable)

Pre-pilot can run on public data only (no Dezerv proprietary inputs). Provider choice does not change workflow or metrics.

Pilot (March)

Governed strategy evolution with versioned artifacts and controlled adaptivity

This pilot does not replace Dezerv's investment process. Atlas becomes the governance layer: it versions strategies, enforces stability constraints, and produces delta-justified changes.

Input: Strategy Versions

Past strategy memos/notes + public signals (optional internal signals later)

Versioned Strategy Diffs

What changed each period, with delta-justifications and confidence

Stability Guardrails

Constraints that prevent uncontrolled driver rotation and narrative drift

Import strategy versions (memos / notes / rules) for the prior quarters (can remain high-level if needed).

Now→March pre-pilot, then 4–6 weeks in March for the full pilot

Week

Activities & Output

Week 0

Pre-Pilot Kickoff (Now)

Pick universe + cadence + strategy shape (high-level)
Define "acceptable stability" and what counts as a meaningful regime shift
Configure Atlas project and signal ingestion

Output: pre-pilot plan + schemas

Week 1

Stability Baseline

Generate strategy trace over time on public data
Measure driver churn and reason volatility

Output: stability scorecard v1

Week 2

Drift Attribution + Constraints

Attribute changes (Data Drift vs Model Drift)
Propose and simulate constraints to reduce instability

Output: drift report + constraint proposal

Week 3

Memo Artifact + Hand-off

Deliver quarterly memo template with diffs and delta-justifications
Decide go/no-go for March pilot

Output: pre-pilot final report + March decision

Week 4

March Pilot (Weeks 1–4/6)

Import strategy versions (memos/notes/rules)
Run governed evolution under constraints with versioned artifacts
Measure driver churn, drift rate, and delta-justifications

Output: pilot results + rollout plan

Cost & Engagement

March pilot investment

Includes

Strategy governance setup (versioning + diffs + triggers)
Stability constraints (persistence + contradiction thresholds)
Drift attribution (data vs thesis vs model)
Delta-based justifications and auditability
Weekly working sessions with Dezerv decision owner(s)
Final report: metrics + recommended rollout scope