Data Pipelines & Integration
Connect AI to your real data — securely and at scale.
AI is only as good as the data it can reach. We build production data pipelines that connect your AI systems to CRMs, knowledge bases, APIs, and internal tools — with MCP-based discovery, governed access, and real-time sync.
Delivery Snapshot
50+
enterprise connectors
< 500ms
sync latency
99.9%
pipeline uptime
Expected outcomes outcomes
Measurable results that improve delivery speed, resilience, and ROI.
Why teams choose us
We connect AI to your real data — not a sanitized demo dataset.
MCP-first architecture
One protocol to connect all tools. Add new data sources in days, not weeks — without rewriting agent logic.
Production-grade reliability
Dead-letter queues, circuit breakers, and automated retries. Your pipelines stay up when downstream systems don't.
Governed by default
PII masking, access logging, and retention policies are built in — not bolted on after an audit finding.
Core capabilities
Production data infrastructure that turns disconnected enterprise tools into a unified AI-ready data layer.
MCP server development
Build custom MCP servers that expose your enterprise tools as standardized capabilities. Agents discover available tools automatically, negotiate access, and call them through a single protocol — no custom integration code per tool.
ETL & streaming pipelines
Ingest data from databases, APIs, file stores, and SaaS tools into vector stores and knowledge bases. Incremental sync, deduplication, schema evolution handling, and data quality scoring at every stage.
API orchestration & webhooks
Coordinate complex multi-system workflows: when a deal closes in Salesforce, trigger document generation, update the knowledge base, notify the support agent, and log the event — all reliably and in order.
Data governance & access control
Role-based access policies, PII detection and masking, data lineage tracking, and retention management. Every data access by every agent is logged and auditable.
Where it applies
Practical scenarios that map to measurable outcomes.
CRM & sales intelligence
Connect AI agents to Salesforce, HubSpot, and Outreach so they can pull deal context, update records, and trigger workflows in real time.
- Real-time deal context for AI copilots
- Automated CRM enrichment from conversations
- Pipeline health scoring with live data
Knowledge base synchronization
Keep RAG pipelines current by syncing Confluence, Notion, SharePoint, and Google Drive into vector stores — with incremental updates, not nightly batch jobs.
- Near real-time document indexing
- Permission-aware retrieval
- Stale content detection and flagging
Operations & internal tools
Connect AI to your ticketing systems, monitoring tools, and internal APIs so agents can take action — not just provide suggestions.
- Jira/Linear ticket creation and updates
- PagerDuty and Datadog integration
- Internal API orchestration for approvals
How we work
A focused, milestone-driven approach that keeps momentum and clarity.
Data landscape audit
Data landscape audit
Map your existing data sources, APIs, access patterns, and governance requirements. Identify which systems AI needs to reach and what access policies apply.
Integration architecture
Integration architecture
Design MCP server topology, pipeline architecture, and data flow patterns. Define schemas, sync strategies, and governance rules.
Build & connect
Build & connect
Implement MCP servers, ETL pipelines, and API orchestration. Wire up data quality checks, monitoring, and alerting at every stage.
Deploy & monitor
Deploy & monitor
Production deployment with pipeline health dashboards, data freshness SLAs, and automated anomaly detection. Ongoing support for adding new data sources.
Frequently asked questions
Answers to common project and collaboration questions.
What is MCP and why should I care?
Can you connect to our legacy on-premise systems?
How do you handle data freshness vs. cost?
Ready to connect AI to your real data?
Let us build the data infrastructure that turns your enterprise tools into an AI-ready, governed data layer — so your agents work with facts, not guesses.