Selected Capabilities

Engineering problems
we've solved.

We present selected engineering capabilities below. We don't fabricate client names, testimonials, or statistics. These are representative examples of real problem domains and architectures we engineer.

On our approach: Client confidentiality is standard practice. Detailed case studies are available to qualified prospects under NDA. The examples below represent our genuine capability areas and technical approaches.

Artificial Intelligence · Enterprise

Intelligent Document Processing & Knowledge Extraction System

An enterprise AI platform designed to ingest, process, and extract structured information from large volumes of unstructured business documents — contracts, reports, invoices, and compliance filings — at scale.

AI Engineering
Challenge
Manual document review consuming hundreds of hours per week. Critical data buried across thousands of files. No structured way to query or audit historical documents.
Approach
Built a RAG pipeline combining OCR ingestion, semantic chunking, vector embeddings, and a fine-tuned extraction model. Multi-agent verification layer for accuracy validation.
Architecture
Document ingestion API → OCR processing → embedding generation → vector store → LLM extraction → structured output → verification agent → API response.
Outcome
Near-instant document query capability. Structured data extraction replacing manual review workflows. Audit trail for every extraction decision.
PythonFastAPILangChainpgvectorOpenAI APIPostgreSQLRedisDockerAWS S3
Enterprise Modernization · API Engineering

Legacy System Modernization & Unified API Platform

End-to-end modernization of a fragmented enterprise technology landscape — replacing siloed legacy integrations with a unified, versioned API platform connecting ERP, CRM, and business intelligence systems.

Enterprise
Challenge
12+ legacy systems with point-to-point integrations. Data inconsistency across platforms. No single source of truth. New features taking months due to integration debt.
Approach
Designed an event-driven API gateway as the central integration hub. Incremental migration strategy allowing legacy systems to be replaced progressively without downtime.
Architecture
API Gateway → Event Bus → Domain services → Legacy adapters → Unified data model → Real-time sync → Business intelligence layer.
Outcome
Consistent data across all systems. New integrations developed in days rather than months. Foundation for AI layer deployment across business domains.
GoApache KafkaPostgreSQLKubernetesGraphQLREST APIsTerraformAWS
Data Engineering · Cloud Infrastructure

High-Throughput Real-Time Data Pipeline & Analytics Platform

Design and deployment of a real-time data streaming infrastructure capable of handling millions of events per hour, with sub-second query latency on historical aggregates and live dashboard capabilities.

Data Engineering
Challenge
Batch processing creating 6+ hour data latency. Analytics queries timing out on growing data volumes. Business decisions based on stale data.
Approach
Replaced batch ETL with streaming architecture. Implemented tiered storage strategy: hot path for real-time queries, warm path for recent history, cold path for long-term analytics.
Architecture
Event producers → Kafka → Stream processing → ClickHouse (hot) + Snowflake (warm/cold) → Query API → BI dashboards + AI models.
Outcome
Real-time dashboard updates replacing 6-hour batches. Sub-second query response on billions of rows. Foundation for predictive analytics deployment.
Apache KafkaApache FlinkClickHouseSnowflakedbtAirflowKubernetesTerraform
SaaS Product Engineering · AI Integration

AI-Powered SaaS Platform with Multi-Tenant Architecture

Full-stack SaaS product engineering — from zero to production — including multi-tenant data isolation, billing integration, AI feature layer, and growth infrastructure designed to scale from early customers to enterprise contracts.

SaaS Engineering
Challenge
New product with no existing infrastructure. Need for enterprise-grade security and compliance from day one. AI features differentiating the product in a competitive market.
Approach
Designed multi-tenant architecture with row-level security. Embedded AI features as first-class product capabilities, not bolted-on integrations. Built metered billing and usage tracking from launch.
Architecture
Next.js frontend → API layer → Multi-tenant DB with RLS → AI service → Feature flags → Billing → Analytics → Admin portal.
Outcome
Production-ready SaaS platform with SOC 2-aligned security practices. AI features driving differentiated user engagement. Clear upgrade path to enterprise contracts.
Next.jsTypeScriptFastAPIPostgreSQLOpenAI APIStripeRedisVercelAWS
What We Do Best

Core capability areas.

AI System Engineering
LLM applications, RAG systems, multi-agent architectures, AI workflow automation, and enterprise AI integration.
Enterprise Modernization
Legacy system transformation, API ecosystem design, ERP/CRM integration, and cloud migration strategies.
Data Infrastructure
Data pipelines, warehouses, lakes, real-time streaming, and analytics platforms for data-driven organizations.
SaaS Product Engineering
Full-stack SaaS development with multi-tenancy, billing, growth infrastructure, and AI feature integration.
Cloud Architecture
Scalable cloud-native architectures, infrastructure automation, DevOps pipelines, and observability systems.
Mobile & Web Products
Native iOS/Android apps, web applications, dashboards, and digital products from concept to production.

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