CognitiveBricks helps enterprises design and deploy decision support systems that combine semantic data, scenario modeling, and agent-assisted analysis—so leaders act on trusted evidence, not fragmented dashboards.
From data to decisions—with guardrails, brick by brick.
Decision support stack
Sources & signals
ERP · CRM · ops · external
Semantic layer
ontology · metrics · lineage
Analytics engine
SQL · graphs · simulations
Decision workspace
scenarios · approvals · audit
Agent orchestration
RAG · tools · HITL · eval
1
Shared semantic layer for analytics, scenarios, and agents
3–5
Typical priority decision domains in first release
6–10 wks
Typical path from blueprint to pilot workspace
Teams have dashboards and copilots—but not a unified system for comparing options, explaining trade-offs, and recording who decided what with defensible evidence.
Metrics differ by team because there is no shared semantic layer for planning, finance, and operations
What-if and scenario models live in spreadsheets disconnected from live enterprise data
Executives get narrative summaries without traceable links to source facts and assumptions
Compliance teams cannot audit AI-assisted recommendations when prompts and data lineage are opaque
Decision workflows stop at insight—approvals, escalations, and outcomes are not captured in one system
Reasoning maturity
Most enterprises stop at dashboards and predictions. We help you climb the reasoning ladder—using governed data, classical AI, and LLMs together so each stage strengthens the next.
What happened?
“What were regional sales and margin last quarter?”
Establish a trusted record of past performance—aggregated metrics, trends, and KPI snapshots everyone agrees on.
Data
Curated marts, semantic metrics, and governed BI views with lineage
AI
Automated aggregation, seasonality detection, and anomaly flags on time series
LLM
Natural-language summaries and Q&A over dashboards—with citations to source metrics
What is likely—and why?
“Which factors correlate with churn, and who is at risk next quarter?”
Move from counts to patterns: statistical inference, probabilistic forecasts, and model-backed estimates grounded in historical evidence.
Data
Feature stores, labeled datasets, and reproducible statistical samples
AI
Predictive models, clustering, uplift baselines, and Bayesian inference pipelines
LLM
Explain model outputs and correlations in plain language—with links to features and cohorts
What if we act?
“If we change pricing in Region A, what happens to margin—and why?”
Answer intervention questions with causal graphs, experiments, and counterfactual reasoning—not correlation dressed up as strategy.
Data
Experiment logs, causal DAGs, treatment/control cohorts, and simulation inputs
AI
Causal inference, do-calculus engines, uplift modeling, and scenario simulation
LLM
Interpret causal pathways, compare interventions, and draft decision memos with stated assumptions
CognitiveBricks builds the semantic layer, knowledge graph, and agent stack so you can progress through all three stages on one platform—without rebuilding your data estate at every maturity step.
Data modalities
High-stakes decisions rarely live in one format. We fuse every data type into a governed decision fabric so leaders see numbers, events, documents, and media in one evidence-backed view.
The system of record
ERP · data warehouse · CRM · ledgers · SQL marts
Tabular, schema-bound data with defined keys and metrics—the foundation for KPIs, scenarios, and causal variables everyone can audit.
Decision role
Anchor descriptive baselines, run what-if simulations, and encode treatment effects with precise, joinable facts.
How we use it
Semantic layer and metric catalog over governed tables; SQL and graph queries feed dashboards, models, and agent tools with lineage intact.
Operational context
JSON events · logs · API payloads · IoT streams · HL7/FHIR
Flexible formats with partial schema—capturing timing, exceptions, and system behavior that pure tables often miss.
Decision role
Explain why a metric moved, surface leading indicators, and tie real-time signals to entities before they appear in monthly reports.
How we use it
Parsing and enrichment pipelines normalize events into graph edges; correlation and stream analytics link signals to customers, assets, and orders.
Narrative & policy context
Contracts · policies · email · PDFs · Confluence · meeting notes
Prose and documents that carry constraints, precedent, stakeholder intent, and qualitative rationale behind the numbers.
Decision role
Ground recommendations in policy, cite contract terms, and capture why a prior decision was made—not just what the dashboard shows.
How we use it
Chunked retrieval with citations, entity extraction into the knowledge graph, and LLM summaries that link every claim to a source document.
Evidence from the physical world
Images · video · audio · scans · diagrams · sensor imagery
Visual, audio, and sensor data that adds field reality—inspection results, customer calls, site photos, and equipment readings.
Decision role
Validate assumptions with ground truth, enrich risk assessments, and give executives context that spreadsheets alone cannot convey.
How we use it
Vision and audio embeddings fused with structured records via shared entity IDs; multimodal RAG lets agents cite a photo, clip, or reading alongside a metric.
The decision workspace links all four modalities to the same entities in your knowledge graph—so one approval packet can cite warehouse numbers, the triggering event log, the contract clause, and the site image in a single auditable thread.
Platform options
The same reasoning maturity, data-modality fusion, and governance patterns apply everywhere—including a sovereign stack anchored on PostgreSQL when public cloud is not an option.
Lakehouse to Bedrock agents
S3 · Glue · Lake Formation · Redshift · Athena · Bedrock · SageMaker
Stand up a governed data foundation on S3 and the AWS analytics stack, then layer semantic models, ML, and agent orchestration with native AWS AI services.
Fabric IQ & OneLake decisions
OneLake · Warehouse · Semantic models · Fabric IQ · Graph · Plan · Fabric agents
Unify data, business meaning, planning, and agents on Microsoft Fabric—ontology, graph, and scenario planning on the same OneLake estate leaders already trust.
Autonomous DW to OCI Gen AI
Autonomous DW · Select AI · Vector search · Object Storage · OCI Gen AI · Vault
Deploy decision support on Oracle Autonomous Data Warehouse with autonomous operations, Select AI, vector search, and OCI Gen AI for production-grade agent assist.
BigQuery to Vertex agents
BigQuery · Cloud Storage · Dataplex · Vertex AI · Gemini · Cloud Run
Build governed decision intelligence on Google Cloud—BigQuery as the semantic core, Dataplex for lineage, and Vertex AI for retrieval, models, and agent orchestration.
watsonx.data to watsonx.ai decisions
watsonx.data · watsonx.ai · Cloud Pak · Db2 · OpenShift · AI Governance
Deploy on IBM Cloud or Cloud Pak for Data—watsonx.data for governed lakehouse analytics, watsonx.ai for grounded copilots, and enterprise AI governance for regulated industries.
Sovereign Postgres decision stack
PostgreSQL · pgvector · PostGIS · dbt · Private LLM · Kubernetes · Air-gapped RAG
Run the full decision-support system on private infrastructure with PostgreSQL as the governed core—semantic schemas, pgvector retrieval, scenario SQL, and self-hosted LLMs without public-cloud dependency.
CognitiveBricks delivers the same decision-support patterns—descriptive to causal reasoning, multimodal fusion, and audit-ready governance—on AWS, Azure, Oracle, GCP, IBM, or a private PostgreSQL estate. We help you choose the anchor platform, then implement brick by brick.
A phased accelerator from semantic baseline to production decision workspaces—aligned to your highest-value decision domains.
Weeks 1–2
Weeks 2–5
Weeks 4–8
Parallel rollout
Book a strategy session with our architects to map your agentic AI roadmap, platform foundation, and first production use case.