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Case study · AnalyticsB2B EnterpriseMulti-source RAGPlanner → Executor

AI Analytics

From data silos to an intelligent company brain.

How we replaced days of manual strategy pack-building with an AI system that plans, executes, and explains growth decisions - connected live to every system of record.

Use case
Decision intelligence · Growth strategy · Marketing ROI
Industry
B2B Enterprise
Stack
RAG · ERP · CRM · Analytics · Competitor intel · BI
Architecture
Planner → Executor · VPC/on-prem deployable
(01) The challenge

Where strategy went to wait.

Days of manual compilation. Recommendations arriving after budgets were locked. A leadership team making growth calls on stale data.

Key data sat in silos - finance in the ERP, customer economics in the CRM, channel performance in analytics, competitor signals scattered across reports nobody had time to read.

When a strategy review came around, an analyst spent days pulling, normalizing, and reconciling numbers before a single recommendation could be made. By the time the deck was ready, the budget conversation had already happened.

The data wasn't missing. The connection was.

01
Days of manual pack-building before every strategy review, compressing the window for actual decision-making.
02
Siloed systems across finance, CRM, analytics, and competitive intel with no shared query layer.
03
Late recommendations arriving after budgets were locked - agility was gone.
(02) The solution

A company brain that plans, then acts.

Connected to every system of record. Available the moment the question is asked.

Rather than building another dashboard, we implemented an AI Company Brain powered by Retrieval-Augmented Generation - like having your best analyst and strategy consultant on call, but continuously wired into your live data. The architecture is a two-stage planner → executor: the AI first reasons about what data is needed and how to get it, then runs the plan, models the scenarios, and surfaces ranked recommendations with full rationale.

System architecture · Company Brain
Active · 24/7
Question→Plan→Execute→Recommend & Act
Connected sources
Finance / ERP - budgets, cash-flow
CRM - LTV/CAC by segment
Analytics - channel ROI
Content repos & campaigns
External benchmarks & competitor signals
Thinking / planning
Question interpretation & source mapping
API call drafting across internal systems
Analysis plan generation
Induction / deduction reasoning layer
Execution & output
Multi-source retrieval & normalization
ROI modelling & cash-flow simulation
Ranked strategies with timelines & rationale
Auto-operationalization (PM tasks, budget, vendor)
Governance
Role-based access & data lineage
Full audit logs per query
VPC / on-prem deployable
Sensitive field masking
NextMachin Company Brain - Planner → Executor RAG, grounded on your systems, your strategy, your data.
Featured implementation
“What is our optimal marketing strategy for next quarter?”
AI planning

Sources identified: Finance · CRM · Analytics · Docs · Competitor intel · Benchmarks

API calls drafted:

finance/budgets?dept=Marketing&period=Q2
crm/cohorts?metrics=LTV,CAC&segments=*
analytics/channels/roi?window=180d
docs/search?q="Q2+strategy"
external/competitors?brands=A,B,C

Analysis steps:

1. Normalize channel ROI by segment
2. Compare against competitor cadence & themes
3. Simulate Base/Stretch spend scenarios with cash-flow impact
4. Synthesize & rank strategies with dates, budget, and forecast
AI output (condensed)
Recommendation: Creator-led social for Segments A & C, supported by targeted paid social and retargeting
Why: Historical ROI strongest in A/C; competitor under-indexing on creator collabs; message-market fit validated by prior assets
Budget: €100k over 11 weeks (60% creators · 25% paid social · 15% retargeting)
Forecast (Base): +20–24% MQLs · CAC ↓12–16% · payback <90 days
Auto-operationalization: PM tasks created, budget allocated in finance app, briefs drafted, vendor procurement opened
(03) The impact

Measured at 60 days.

Sustained through the quarter. Same-day recommendations instead of multi-day packs. Budget conversations happened with live data in the room.

Reduced
60–80%
Decision lead time

Multi-day pack → same-day recommendation.

Improved
15–25%
Marketing ROI

Via scenario-tested channel reallocation.

Reduced
10–20%
Customer acquisition cost

Shifting budget from underperforming channels.

Saved
6–10 hrs
Per analyst, per week

From automated retrieval & synthesis.

Improved
10–20%
Forecast accuracy

From consistent data lineage & reconciliation.

Adoption
>65%
Decisions citing AI brief

Quarterly strategy decisions grounded in the AI.

Observed ranges from comparable enterprise deployments. Actuals depend on data quality, maturity, and channel mix.
(04) Testimonial · Head of Growth
“We stopped waiting for the analysis and started having the conversation. The question and the answer are now in the same meeting.”
Head of Growth · B2B Enterprise · Multi-division, multi-market organisation
Engagement reference available under mutual NDA. Public version of metrics shared with permission.
Next step

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