Managed Business Intelligence + Agentic DecisionOps
Turn your company data into a daily AI business intelligence system.
We securely connect to your approved business platforms, structure your data every night, create trusted analytics and forecasting layers, and add business memory, approval workflows, and source-backed recommendations so your team can ask questions, approve actions, and track ROI every month.
Ask like ChatGPT
Answers from company data
Nightly structured BI
Human-approved recommendations
Client question
What changed overnight?
Trust state
VERIFIED
Current workflow
Revenue variance review
Approval needed
Revenue dropped 18 percent against the prior 7-day average. Main drivers are lower ad spend, weaker conversion, and two constrained inventory lines.
EvidenceNightly refresh
DecisionRecommend action
ControlHuman approval
01Old business dataCRM, finance, ads, orders, support, documents
02Nightly data pipelinesClean, validate, join, refresh, monitor
03Decision intelligenceMetrics, forecasts, risks, patterns, ROI signals
04Agent recommendation layerBusiness memory, approved tools, approval controls, audit
How old data becomes ROI
Your data has value only when it improves decisions.
Most companies already have years of data, but it is scattered across tools, spreadsheets, documents, and old systems. The problem is not that they lack data. The problem is that the data is not structured, trusted, refreshed, or connected to decisions and action.
Understand the business first
Identify the decision or workflow that can create measurable value: revenue growth, cost reduction, risk reduction, faster approvals, retention, or better investment allocation.
Map the old data
Find where the data lives across CRM, ecommerce, accounting, ads, inventory, support, documents, policies, spreadsheets, and historical decisions.
Build the secure platform
Create data contracts, quality checks, identity resolution, lineage, access controls, and decision-ready datasets before any agent can act on the business.
Run nightly pipelines
Refresh approved sources, clean and validate records, build structured intelligence layers, and flag stale, missing, duplicated, or suspicious data.
Find patterns and ROI signals
Use analytics, forecasting, risk scoring, anomaly detection, and evaluation to show what changed, where money is wasted, and what decision should happen next.
Recommendations wait for approval
The system uses business memory, approved tools, business rules, and fresh data to prepare recommendations. High-risk actions wait for human approval.
This is not generic AI setup.
The real work is business understanding, data engineering, secure platform design, analytics, evaluation, business memory, approval workflows, governance, and monthly operations. That is why this is a managed intelligence system, not a quick AI install.
Nightly operating model
The system keeps the business ready for better decisions every day.
Teams should not rely on stale chat history or random document search. They need a refreshed decision-ready layer that is automatically structured, validated, and monitored before recommendations are made.
What runs every night
Source syncApproved systems refresh
Data engineeringClean, dedupe, join, validate
StorageRaw, cleaned, and business-ready layers
AnalyticsMetrics, trends, risks, patterns
ML and forecastingPredictions and scenario signals
Business memoryPolicies, decisions, approvals, playbooks
OperationsHealth checks, alerts, ROI reporting
00:15
Nightly data refresh
Pull approved systems, validate fields, check freshness, and store raw evidence.
01:00
Decision-ready data build
Create clean datasets, metric layers, data quality reports, and trusted business views.
02:00
Analytics and forecasting
Run KPI calculations, anomaly detection, risk scores, forecasts, and ROI opportunity analysis.
07:00
Agent briefing and decisions
Explain what changed, recommend actions, prepare approval notes, and log evidence.
Source-backed intelligence
The company data tells the real story. AI explains it.
The system does not let AI invent business numbers. Metrics, forecasts, diagnostics, and recommendations are grounded in approved data pipelines, business-ready datasets, approved metric definitions, and trusted calculation paths. The AI helps explain the story, compare options, draft recommendations, and coordinate workflows, but company data remains the source of truth.
If a question is fully supported, the system returns a verified answer. If data is missing, stale, or not trusted, it returns a provisional or blocked response with the missing prerequisites clearly explained instead of guessing.
Data calculates the facts
Revenue, spend, margin, churn, inventory, support, and operational signals are calculated from approved data sources, nightly pipelines, and structured business datasets.
AI explains and recommends
The AI interprets patterns, explains changes, drafts next actions, and coordinates workflows. It does not invent numbers or replace approved business systems.
Humans approve risk
High-risk actions such as budget changes, pricing decisions, finance decisions, customer treatment, and operational changes are routed to humans before execution.
Trusted answers
Every answer is checked before it reaches your team.
The system does not simply send your question to an AI model. It checks whether the question is supported, retrieves approved business data, calculates the facts, reviews freshness and quality, then prepares a source-backed recommendation with approval and audit history.
Question support check
The system checks whether the business question can be answered from available, trusted, and current data.
Approved data retrieval
Only the data needed for the question is retrieved from approved sources and permissioned datasets.
Source-backed analysis
Metrics, forecasts, risks, and patterns are calculated from business data before the AI explains the result.
Quality and freshness review
Important answers are checked for data freshness, source coverage, and consistency before being shown.
Evidence record
The answer stores what data was used, what recommendation was made, and what approval or action followed.
Recommendation and approval
The system recommends next actions, but high-risk decisions wait for human approval.
How the managed system works
Data Engineering → Platform Engineering → ML / Analytics / Evaluation → Agentic AI Decisioning.
This is the managed operating model behind the client interface. Data engineering creates decision-ready intelligence, platform engineering makes it secure and observable, analytics and ML measure patterns and ROI, and agentic AI orchestrates recommendations, approvals, and safe actions.
How the managed system works
Data Engineering
Build decision-ready context from approved business systems, documents, events, and operational records before recommendations can use the data.
- Source inventory and business data map
- Raw, cleaned, and business-ready intelligence layers
- Data quality, lineage, and contracts
- PII classification and access boundaries
Business workflowROI target
Old dataApproved access
Nightly pipelinesDecision-ready context
Analytics and MLPatterns and forecasts
AI recommendation workflowMemory, tools, approval
Managed DecisionOpsSecurity, evaluation, ROI
Client interface
Ask questions like ChatGPT. Get answers from your company data.
The frontend can be a web app, dashboard, Slack, Teams, or a custom chat interface. Your team asks business questions in plain English. Behind the interface, the system retrieves trusted data, checks business memory, applies approval rules, prepares recommendations, routes human approvals, and logs every important answer and action.
Business question
Why did revenue drop this week, and where should we invest next?
Trust state
VERIFIED
Revenue dropped 18% versus the prior 7-day average.
Orders, ad spend, conversion rate, inventory, and support data were checked.
Lower paid spend, weaker conversion, and two constrained inventory lines explain most of the drop.
Do not increase ad spend until inventory coverage is reviewed.
Proposed action
Create an operations task to review stock and campaign constraints.
Agents do not invent business facts
Metrics, forecasts, and decision inputs come from approved data layers, approved metric definitions, and trusted calculation paths. The agent explains, recommends, and prepares action.
Business memory is governed, not random chat history
Important context is tied to business owners, dates, permissions, and review cycles so recommendations stay grounded in current operating reality.
Monthly management is the recurring value
Pipeline health, data quality, security, evaluation, business memory review, workflow improvements, cost control, and ROI reporting are managed continuously.
Decision memory
The system remembers what was recommended, approved, and what happened after.
Business intelligence should not reset every month. The system records important questions, evidence, recommendations, human approvals, actions taken, and measured outcomes so future recommendations can learn from what actually created value.
Recommendation history
Important recommendations store the question, data used, evidence, trust state, expected impact, and approval requirement.
Action memory
Approved actions are logged with owner, timestamp, target workflow, reason, approval state, and rollback notes.
Outcome tracking
The system compares expected impact against actual results after 7, 30, or 90 days so management can see what created profit, saved cost, or reduced risk.
Office business intelligence
Give the team one place to understand what changed and what to do next.
Instead of managers checking spreadsheets, dashboards, ad platforms, CRMs, finance exports, support tools, and documents separately, the system creates a daily decision layer that explains what changed overnight and prepares the next action for approval.
Daily business briefing
Revenue, spend, customer issues, inventory, operations, risks, and priorities are summarized from trusted data every morning.
Decision queue
Important recommendations are organized by risk, business owner, expected value, and approval status.
Action and ROI tracking
Every recommendation, approval, rejection, task, and outcome is logged so management can see what created value.
Business questions
Ask business questions like ChatGPT. Get source-backed answers from company data.
The system works across industries because the pattern is the same: structure the data, calculate the facts, identify the pattern, recommend the action, ask for human approval when risk is high, and measure the outcome.
Growth intelligence
Where should we invest next month?
Compare revenue, spend, conversion, margin, inventory, customer signals, and capacity to recommend where the next dollar should go for better ROI.
DecisionInvest, pause, investigate, or escalate
DataRevenue, ads, margin, inventory, support, orders
AI actionPrepare ROI recommendation and approval note
ROI metricRevenue recovered, waste avoided, margin improved
ROI model
See how better decisions can fund the monthly managed service.
Clients pay when the system saves time, prevents leakage, reduces risk, improves approvals, and helps teams invest money where the data shows stronger ROI.
Estimated net monthly value
A$33,417
Gross value A$38,417 minus managed service cost A$5,000.
How we work
Build the intelligence system once. Operate it every month.
We do not sell a one-off chatbot or dashboard. We first understand the business, then build the secure data foundation, nightly BI layer, analytics, business memory, approval workflow, audit logs, and ROI reporting. After launch, we operate and improve the system monthly.
Data-to-ROI Blueprint
We map the business workflow, approved data sources, security requirements, decision opportunities, and ROI model before building the system.
- Business workflow and decision map
- Approved source and data readiness review
- Security, governance, and access plan
- System architecture and ROI roadmap
Managed Intelligence System Build
We build the client's intelligence system: approved source connections, nightly data pipelines, trusted metrics, analytics, forecasting, business memory, human approval, audit logs, ROI reporting, and the client interface.
- Approved source connectors and nightly pipelines
- Decision-ready business intelligence layer
- Analytics, forecasting, and ROI dashboards
- Client interface, business memory, and approval workflow
Managed DecisionOps Retainer
After launch, we operate, secure, evaluate, improve, and expand the system every month so it stays accurate, useful, and connected to business outcomes.
- Pipeline, source health, and data quality monitoring
- Security, access, and governance reviews
- Business memory, evaluation, and workflow improvements
- Monthly ROI reporting and new business questions
Architecture proof
Based on real business intelligence and agentic decision architecture work.
Prior architecture work connected ecommerce, marketing, analytics, support, finance, and operations data into nightly business intelligence layers with source-backed metrics, answer trust states, audit records, safety boundaries, and controlled agent workflows.
DataRaw data turned into business-ready intelligence
TrustVerified, provisional, blocked answers
AIAI explains; business systems calculate truth
GovernanceApproval, audit, and decision history
Enterprise safety story
The moat is governed source-backed intelligence, not generic automation.
No open-ended access
Agents only use approved datasets, scoped tools, row limits, redaction rules, and least-privilege permissions.
No invented metrics
Business facts come from approved definitions, trusted calculations, and monitored data quality checks.
No silent action
Risky recommendations require human approval, explicit ownership, cooldowns, safety controls, and audit records.
No unmanaged drift
Every workflow is evaluated, reviewed, cost-monitored, improved, and reported through monthly operations.
Work with me
Start with one business workflow where your data can prove ROI.
We map the workflow, inspect approved data sources, define controls, estimate ROI, and decide whether a managed intelligence system is worth building.