We turn fragmented data into production-grade pipelines, and make the AI and agents on top of them reliable, governed, and safe to ship. Contract-first ingestion, bad records quarantined automatically, and PII masked before anything reaches a model.
Jumping straight from raw operational databases to LLM copilots or automated workflows creates expensive hallucinations and security leaks. Before your models can reason, your data layer must be engineered for trust.
Unannounced upstream application changes silently break downstream executive dashboards and corrupt AI retrieval context windows with malformed records.
Critical business context is trapped across isolated Postgres databases, payment systems, and CRMs with brittle 24-hour batch reporting lags.
Feeding raw operational tables into embeddings or LLM prompts without automated Silver/Gold PII masking exposes sensitive customer and financial data.
Unmonitored spaghetti scripts and oversized proprietary data warehouses drain engineering hours and inflate monthly cloud compute bills.
We don't "vibe-code" data pipelines. Every engagement is governed by our open-source Human-AI Development Model, translating top-tier architectural rigor into four ironclad client guarantees.
Every data source is locked behind explicit schema and semantic data contracts at the ingestion boundary. Malformed records are quarantined automatically before reaching dashboards or AI agents.
Pillar: "Contracts Before Code"Highest-level architectural design paired with deterministic, agent-accelerated implementation cuts delivery timelines and consulting overhead without sacrificing production rigor.
Pillar: "Humans Define Correctness; Agents Ensure Consistency"No pipeline is handed over without automated data quality assertions, end-to-end latency SLAs, and cloud-compute cost profiling proven in staging.
Pillar: "Measure Before Claim"Every architectural change enters through a traceable specification gate, eliminating shadow pipelines, unapproved schema mutations, and consulting scope creep.
Pillar: "Intent Enters Through a Defined Gate"Chakraview is led by a distinguished engineer and architect with 25 years building large-scale data and distributed systems at companies including Capital One, Broadridge, Microsoft, and F5 Networks. As a Distinguished Engineer, our principal built real-time fraud-decisioning platforms, distributed systems and custom databases for Microsoft Azure's software-defined networking, and large-scale, security-sensitive network systems, and set enterprise data and security standards adopted by more than 50 teams. Chakraview publishes open reference architectures for zero-trust, data mesh, and agent governance.
You work with that engineer directly. No account layer, no handoff to juniors.
Whether you need a rapid diagnostic to unblock your AI roadmap, a turnkey modern lakehouse foundation, or top-tier contract execution, we offer structured engagements with zero billing surprises.
Best for SMBs preparing to deploy AI agents, RAG pipelines, or executive analytics who need a clear architectural blueprint and risk audit.
Best for growing businesses ready to replace brittle scripts with a governed, low-ops Medallion Lakehouse that powers analytics and AI.
Best for engineering leaders needing hands-on Technical leadership for high-throughput streaming, CDC, or critical platform contracts.
For teams putting AI agents into production. We build the layer that makes an autonomous, non-deterministic system safe to run.
Proof: our plain-language guide to running AI agents in production, plus our distributed systems patterns for agentic workloads and zero-trust Kubernetes blueprint, covering attested agent identity, capability policy the agent cannot edit, and on-behalf-of delegation.
Discuss an Agent Reliability EngagementExplore our production reference platform. We apply the exact same Contract-First Medallion principles across two deployment tracks tailored to your operational footprint.
Don't have a dedicated 10-person DevOps team? We deploy our Contract-First Medallion architecture on object storage using Apache Iceberg + DuckDB / AWS Athena. You get enterprise schema enforcement, automated PII masking, and clean AI-ready Gold tables with near-zero infrastructure overhead.
Need sub-second operational pipelines? Full streaming topology featuring Debezium CDC, Apache Kafka, stateful Apache Flink processing, and ACID-compliant Iceberg tables, engineered for strict schema evolution, dead-letter quarantining, and high-throughput analytical parity.
Contract-driven Kappa streaming lakehouse (Debezium CDC → Kafka → Flink → Apache Iceberg → DuckDB). Demonstrates schema enforcement at ingestion, dead-letter quarantining, and sub-second Gold-layer analytical queries.
Decentralized domain ownership on Apache Iceberg with federated compliance controls (PCI-DSS, AML, SOX), automated PII masking, and cross-domain zero-copy analytics.
Production-informed reference blueprints and decision matrices covering 9 core data engineering patterns across 22 idiomatic stack variants, with trade-off criteria for choosing the right architecture.
35+ production patterns for AI agent networks, covering agent identity, delegation chains, token budgets, failure recovery, and consistency for autonomous, non-deterministic workloads.
Production-grade zero-trust reference implementation: workload identity (SPIFFE/SPIRE), mTLS, OPA policy-as-code, and a zero-trust gateway, extended to govern AI agents as policy-bounded workloads.
Phased migration of enterprise monoliths to Kubernetes microservices using event sourcing, CQRS, sagas, and SLA-as-code, with contracts defining correctness.
Request a 45-minute introductory architecture review with our Chief Architect. No sales fluff, just a direct technical evaluation of your current stack, bottlenecks, and AI goals.
1. Submit your stack profile using the intake form.
2. We review your architecture and reply within 1 business day with a calendar link.
3. Walk away from our 45-minute call with clear next steps, whether we engage together or not.