AI Agents Infrastructure & Engineering Partner

Powering Production-Grade
AI Agents

We engineer the context pipelines, data systems, and cloud infrastructure — on Redis, MongoDB, and AWS — that turn AI prototypes into scalable, reliable, and cost-efficient production systems.

ContextDataCloudPerformanceControl

Engineered pipelines that eliminate hallucinations

Most AI agents fail in production due to poor context, slow data systems, or runaway cloud costs. We fix that.

A Structured Path to Production AI

Production AI Readiness Program

Go from prototype to production in 4–8 weeks.

A structured engagement for high-growth teams scaling AI systems without accumulating architectural debt.

Infrastructure Audit

Deep dive into your data layer, performance bottlenecks, and cloud costs.

Context Pipeline Design

Design memory and retrieval systems for accuracy and reliability.

Scaling Roadmap

A proven path to low-latency, high-reliability production systems.

Cost Control

Reduce token usage and infrastructure overhead by 30%+.

Outcome: A production-ready AI system that is fast, reliable, and fully observable.

Start Your Readiness Assessment

Designed for production-scale AI systems

Delivered through focused 4–8 week engagements

Solutions

What We Deliver

Proven migration paths, scaling strategies, and security hardening — ready to deploy.

Recommended

Production AI Readiness

A focused 4–8 week engagement to turn your AI system into a fast, reliable, and production-ready platform at scale.

Infrastructure AuditContext EngineeringScaling ArchitectureCost Optimization
Start Your Readiness Assessment →
New

Pinecone → Redis Cloud

Migrate AI vector search workloads from Pinecone to Redis Cloud — hybrid search with native BM25 scoring, VPC peering, index mapping, and query translation via RedisVL.

PineconeRedis CloudRedisVLHybrid SearchBM25
Read the Migration Guide →
New

MongoDB → Redis for AI

Move latency-sensitive AI workloads — session memory, semantic cache, context retrieval, and vector search — from MongoDB to Redis while keeping MongoDB as your system of record.

MongoDB AtlasRedis CloudDebeziumVector Search
Read the Migration Guide →
Migration

ElastiCache → Redis Cloud

Zero-downtime migration with guaranteed data integrity and validated performance.

AWSRedis Cloud
Migration

Couchbase → Redis via MSK

Stream-based migration using Amazon MSK with real-time sync and zero data loss.

CouchbaseMSKRedis Cloud
Migration

MongoDB Community → Atlas

Seamless upgrade to fully managed Atlas with automated backups, scaling, and Atlas Search.

MongoDBAtlas
Migration

Oracle → MongoDB

Legacy workload migration with schema redesign, data transformation, and Redis read-optimization.

OracleMongoDB AtlasRedis
Scale & Control

Real-Time Sync (Debezium)

CDC pipelines to keep Redis, MongoDB, and downstream systems in perfect sync.

DebeziumKafkaRedisMongoDB
Scale & Control

Redis Caching Layer

Add Redis in front of Oracle, MongoDB, or any primary datastore to accelerate reads and cut database load.

Redis CloudOracleMongoDB
Scale & Control

Kubernetes for AI

Containerized EKS pipelines with auto-scaling and rolling zero-downtime updates.

KubernetesAWS EKSHelm
Scale & Control

S3 Backup & Disaster Recovery

Automated backup pipelines with point-in-time recovery and cross-region replication.

AWS S3MongoDBRedis
Scale & Control

Context Pipeline Design

End-to-end context engineering — refinement, labeling, synthetic data, and memory lifecycle for AI Agents.

LLMsRAGMongoDB Atlas Vector Search
Security

Security & IAM Hardening

Least-privilege IAM, VPC peering, encryption at rest and in transit, and compliance audit support.

AWS IAMVPCKMS
Scale & Control

Performance Benchmarking

Complete P50/P90/P99 profiling and continuous regression testing for production stability.

New RelicDatadogGrafana

How We Work With You

Programs Built for AI Agent Teams

From your first production audit to ongoing infrastructure support — choose the engagement that fits your stage.

Entry2 Weeks

AI Agent Production Audit

Know in 10 business days why your AI agent will break in production — and exactly how to fix it.

  • Architecture & context pipeline review
  • Latency baseline & cost analysis
  • Prioritized production roadmap
Get Started →
Most Popular
Flagship4–8 Weeks

Production AI Readiness Program

Go from fragile prototype to production-grade agent platform.

  • Full infra audit & context pipeline design
  • Observability, benchmarking & SLA setup
  • Cost optimization & production hardening
Get Started →
RetainerMonthly

Production AI Support Plan

Keep your AI agents fast, reliable, and cost-controlled every month.

  • Performance reviews & regression testing
  • Cloud cost tracking & optimization
  • Incident advisory & architecture recs
Get Started →

Technology Ecosystem

We Have Expertise In

Built on the platforms that power modern AI — Redis, MongoDB, and AWS at the core.

Redis CloudCore Platform
MongoDB AtlasCore Platform
AWSCore Platform
KubernetesOrchestration
New RelicObservability
DatadogObservability
GrafanaObservability

Why Partners Choose Us

100%

Visibility into your Agent's
memory, latency & cost

Real-Time

We implement & manage the
observability stack you choose

“Faster and cheaper
— simultaneously.”

Performance optimization that
pays for itself

About Us

Engineering the Backbone of Production AI

We don't just build AI — we build the infrastructure layer that makes AI production-ready, specializing in Redis, MongoDB, and AWS.

Most AI projects fail when moving from demo to production — due to context bottlenecks, data latency, or uncontrolled cloud costs. We solve this by combining Context Engineering with Cloud-Native Performance across the stack that modern AI runs on.

We ensure your AI agents are not just intelligent, but fast, reliable, and fully observable at scale. Based in India, we serve teams globally with deep expertise in high-throughput, real-time systems with strict latency and reliability requirements.

Our Foundation

15+ Years of distributed systems expertise.

Deep Specialization

Redis, MongoDB, and AWS architecture.

Proven Migration Paths

For high-throughput, real-time workloads.

Blog

Latest Thinking

AI AgentsAI InfrastructureObservabilityLLMRAGMonitoringRedis CloudMongoDB AtlasAWS BedrockProduction AI

Monitoring Your LLM RAG Pipeline: A Practical Guide to Observability That Actually Matters

Your RAG agent answers questions, but can you tell when it is slow, expensive, or wrong? This post breaks down the three pillars of LLM pipeline observability — tracing, metrics, and evaluation — with real latency numbers, cost breakdowns, and concrete examples from a production agent running DeepSeek v3.2, Amazon Titan Embed v2, Redis Cloud, and MongoDB Atlas.

2026-04-1116 min read
AI AgentsSemantic CachingSimilarity ThresholdEmbeddingsRedis CloudAWS BedrockCost OptimizationVector SearchLLMProduction AI

How to Decide the Semantic Similarity Threshold for Your AI Cache

The similarity threshold is the single most impactful setting in a semantic cache. Set it too high and you pay for duplicate LLM calls. Set it too low and you serve wrong answers. This post walks through a real experiment — 14 query variants, two embedding models, and hard data — so you can pick the right number for your use case.

2026-04-1014 min read
AI AgentsSemantic CachingLLMCost OptimizationRedis CloudMongoDB AtlasEmbeddingsAI InfrastructureVector SearchProduction AI

Semantic Caching for AI Agents: Why It Matters, When to Use It, and What It Actually Saves

Every LLM call costs tokens and time. Semantic caching reuses answers for similar questions — cutting costs by 60-90% for repetitive workloads. Here's how tokens, embeddings, and inference actually work, when caching makes sense, and how to implement it with Redis and MongoDB.

2026-04-1012 min read

Let's Talk

Ready to Scale Your Agentic Stack?

Whether you're building your first AI agent or scaling to production — tell us where you are and we'll find the right path forward.

Building an AI agent and need help? We've got you covered — from first audit to production support.