AI-ready data pipelines
Reliable ingestion, cleaning and enrichment that turns scattered sources into AI-consumable assets.
AI Infrastructure
Data & Knowledge Engineering
Trustworthy AI starts with trustworthy data. We build the pipelines and knowledge layers that ground your systems.
- 10×faster access to institutional knowledge
- 99%pipeline reliability with contract tests
- 1governed source of truth for AI workloads
Capabilities
The data layer your AI is missing
Pipelines, vector stores, knowledge graphs and enterprise data foundations for AI.
Knowledge graphs
Entities and relationships made explicit — so AI can reason over how your business actually connects.
Semantic and metadata layers
Shared definitions of customers, products and metrics that keep every AI answer consistent.
Governance and lineage
Access controls, quality monitoring and full lineage from source to AI output.
How it works
From scattered data to structured knowledge
Model quality is capped by data quality. We build the pipelines, graphs and semantic layers that raise the cap.
Pipelines
Ingestion that doesn't wake anyone at 2am
Connectors into your databases, SaaS tools, documents and event streams — with schema contracts, quality checks and monitoring so downstream AI never trains or answers on broken data.
- Batch and streaming ingestion with schema contracts
- Data quality gates that quarantine bad records
- Full lineage from raw source to AI-ready asset
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Knowledge graphs
Relationships your embeddings can't see
Who owns which account, which part goes in which product, how policies relate to controls — explicit graphs give AI multi-hop reasoning that vector similarity alone can't deliver.
- Entity resolution across systems and naming chaos
- Graph-augmented retrieval for multi-hop questions
- Ontologies co-designed with your domain experts
Shared stateHandoffsRetriesTraces
Semantic layer
One definition of 'active customer'
When metrics and entities mean the same thing everywhere, AI answers stop contradicting your dashboards. We encode business definitions once and serve them to every model and agent.
- Governed metric and entity definitions
- Consistent joins between structured and unstructured data
- Access policies enforced at the semantic layer
DocsCRMWiki
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Answer with citationsscoped to the user's permissions [1] [2]
Use cases
Where data & knowledge engineering delivers value
RAG foundations
Clean, deduplicated, permission-tagged corpora that make retrieval accurate.
Customer 360 for AI
Unified customer entities powering personalization, support and churn agents.
Product knowledge bases
Specs, compatibility and documentation structured for agents and configurators.
Risk and compliance graphs
Regulations, controls and evidence linked for auditable compliance AI.
Operational data products
Curated, contracted datasets that AI and analytics teams consume self-serve.
M&A and entity intelligence
Company, ownership and relationship graphs for diligence and monitoring.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Every AI initiative we tried stalled on the same thing: nobody trusted the data. Six months after the knowledge layer went in, three agent projects shipped on top of it.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Snowflake
- Databricks
- BigQuery
- dbt
- Airflow
- Kafka
- Neo4j
- Postgres
- Elasticsearch
- Pinecone
- Great Expectations
- DataHub
FAQ
Common questions
Do we need to fix all our data before doing AI?
No — that's a trap. We scope the data work to the AI use cases that matter: clean and structure what the first workflows need, prove value, then expand. Perfection-first programs die before shipping.
When is a knowledge graph worth it?
When questions require multi-hop reasoning — 'which customers are affected by this part's recall?' — or when entity identity across systems is the core problem. For pure document Q&A, tuned retrieval usually suffices.
Can you work with our existing warehouse and tools?
Yes. We build on Snowflake, Databricks, BigQuery and your current stack — adding the AI-specific layers (knowledge structure, semantic definitions, permission-aware serving) rather than replacing what works.
How do you keep the knowledge layer current?
Automated sync with change detection, quality gates and freshness monitoring. Stale or broken sources raise alerts before they poison downstream answers.
Next step
Build the foundation your AI deserves
Tell us what your AI keeps getting wrong — the fix is usually in the data layer.