Delivery intelligence system
for AI-native teams.

Spec-driven. Fully verified. Smarter every sprint.

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Software Factory|Azure + Fabric + Power BI
live · last updated 2h ago

Delivery Intelligence

DAX: use CALCULATE, never nested FILTER
Fabric pipelines → Delta Lake format only
Star schema: fact_ and dim_ prefixes
Datasets > 1M rows → Spark, never pandas

Agent Mistakes \u2192 Permanent Fixes

Never hardcode connection strings
SELECTEDVALUE needs fallback in measures
No direct SQL in Fabric notebooks — use Spark SQL
Incremental refresh requires partitionKey column

After-Code Rules

All PRs require security scan before merge
Deploy only through Azure DevOps pipeline
Performance test datasets > 500k rows before ship
Documentation diff required for schema changes

Rule Coverage

Spec
52
Harness
44
Verification
38
Compound
13

Agent Trace | Auditable

Read specdone
Load harness (147 rules)done
Plan (4 files, 2 DAX measures)done
Validation (6/6 passed)passed
Security scan (clean)clean
PR submitteddone
Self-Optimization Loop
Prompt accuracy91.2% → 94.7%
Token efficiency−32% cost
Rule conflicts resolved7 this sprint
Coverage delta+4.2%
Last evolved4h ago · auto
Pipeline:healthy
Drift:none
Queue:2 tasks
Last deploy:47m ago
0 rules0 teams0 sprints0 auto-optimized
+12 rules this sprint

01 The Insight

AI covers 30% of delivery. We cover 100.

Most AI tools stop at code generation. That's only 30% of what it takes to ship reliable software. The other 70% — testing, security, compliance, deployment, monitoring — is where delivery breaks down.

30% Writing code
70% Testing · Security · Compliance · Deployment · Monitoring

The factory covers the full lifecycle. Testing agents. Security agents. Deployment orchestration through Azure DevOps and Fabric pipelines. A delivery knowledge graph connecting your code, services, Power BI workspaces, Databricks notebooks, and policies into one system your agents reason over. Not just the 30% that Copilot touches. The whole thing.

02 Delivery Equity

Build equity, not debt.

Technical debt slows you down over time. Delivery Equity speeds you up. Every sprint adds permanent intelligence: harness rules, spec patterns, optimization data. It compounds across teams and self-improves through automated evolution algorithms.

Day 1
0
rules

Empty. Same mistakes repeated.

Day 30
0
rules

Patterns absorbed. New engineers productive immediately.

Day 90
0
rules

Cross-team. Dramatically better output.

Continuous
∞
self-evolving

Prompts and architectures evolve from data.

Everyone has the same models. Nobody has your Delivery Equity.

03 The System

From spec to production. Every decision traced.

Three pillars. One compounding system. AI-Native Specs define what to build. Harness Engineering traces every decision. Closed-Loop Verification ensures nothing breaks. Together, they form the delivery intelligence layer. 

spec:
  data_sources:
    - salesforce_opps
    - hubspot_deals
  measures:
    - revenue_mtd
    - pipeline_coverage
  slicers: [region, segment]
  visuals: [bar, kpi_card]
  refresh: 6h
  deploy_to: fabric_prod
Pillar 1: AI-Native Spec

Spec-Driven Development

Machine-readable specifications. The agent knows exactly what to build, what to validate, and where to deploy. No ambiguity survives to production.

steptokensdur
load_harness—12ms
parse_spec3400.8s
generate_dax2,1403.1s
run_tests8701.4s
security_scan4100.6s
submit_pr—0.2s
Pillar 2: Harness Engineering

Full Observability and Audit Trail

Every decision traced. Every prompt versioned. Token costs visible per step. Full audit trail for compliance.

  • Azure DevOps build
  • Unit tests 42 passed
  • Integration tests 18 passed
  • SAST scan 0 findings
  • API security passed
  • Fabric pipeline sync
  • Power BI deploy live
Pillar 3: Closed-Loop Verification

Full Lifecycle Delivery

Testing, security, Fabric orchestration, Power BI deployment. The 70% most tools ignore. Every commit verified before it touches production.

candidates evaluated16
best accuracy94.2%
token reduction-38%
methodDSPy MIPRO v2
insight Chain-of-thought on step 3 adds 400 tok but no accuracy gain. Removed.
The compound layer

Self-Improving Prompts

Analyzes traces, diagnoses root causes, evolves better prompts automatically. This is how the system gets smarter every sprint.

04 Evidence

Who proved it. And what they found.

These aren't projections. Real companies, real numbers, real production systems. The pattern is consistent: methodology beats raw tooling every time.

Ramp 50%+ merged PRs from agents

$13B fintech. Organic adoption. PMs ship code. Open-source harness approach.

StrongDM 0 human-written lines

Specs drive everything. Digital Twin validation replaces review.

OpenAI 1M+ lines, 3 engineers

Codex. 1/10th the time. Validated at scale.

Industry 200K+ Copilot licenses

TCS, Infosys, Wipro moving. 74% of deals AI-themed.

METR -19% experienced devs slower

Randomized controlled trial on open-source contributors. Devs perceived 20% gain. Measured 19% slower. The gap is methodology.

Walk away with a plan. Whether you use us or not.

30-minute Delivery Blueprint Session. We scope it, price it, and hand you an implementation plan you can execute tomorrow.

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