Technology Stack

Built on the World's Leading Platforms

We leverage elite cloud infrastructure, pioneering AI models, and high-performance development frameworks to build secure, production-grade enterprise software.

Discuss Your Tech Stack
Platform Stack

Supported Technologies & Frameworks

We design and deploy architectures utilizing top-tier technologies, selecting the optimal combination for your regulatory, scaling, and cost constraints.

Foundational Model

Anthropic Claude

Expert implementations using the Claude 3.5 model family. We build custom agentic loops, optimize prompt pipelines, and deploy secure models for enterprise tasks.

Spec: 200K Context • Advanced Reasoning
Cloud Infrastructure

Amazon Web Services

AWS-powered enterprise cloud hosting, serverless computing (Lambda, ECS), and Amazon Bedrock integration to deploy private foundation models inside secure VPC configurations.

Spec: Private VPC • AWS Bedrock • 99.99% Uptime
Data & ML Pipelines

Google Cloud Platform

Advanced machine learning pipelines built on Vertex AI, fast semantic search over BigQuery vector search indices, and secure Kubernetes (GKE) container management.

Spec: Vertex AI • BigQuery Vector • GKE Clusters
Enterprise Cloud

Microsoft Azure

Enterprise-grade compliance configurations using Azure OpenAI Service, AKS deployments, and Active Directory SSO integrations for regulated industries.

Spec: Azure OpenAI • Active Directory SSO
Foundational Model

OpenAI

Structured reasoning loop implementations with GPT-4o, custom fine-tuning API workflows, and low-latency audio integrations with the Whisper API.

Spec: GPT-4o & o1 • Structured JSON schema
Frameworks

AI Orchestration

Orchestrating complex autonomous agent networks and memory chains using LangChain, LangGraph, and advanced semantic retrieval indexes via LlamaIndex.

Spec: LangGraph Multi-Agent • LangChain RAG
Semantic Database

Vector Search & Memory

Architecting long-term memory structures for AI agents utilizing Pinecone, Milvus, Qdrant, and PGVector for sub-second retrieval of enterprise knowledge.

Spec: Pinecone & Qdrant • Sub-10ms retrieval
Frontend & Edge

Vercel & Next.js

High-performance, edge-optimized frontends built with Next.js, hosting real-time streaming LLM UI completions and serverless middleware APIs.

Spec: Next.js App Router • Edge streaming
Our Standard

Architectural Principles

We hold our architectural standards and platform integrations to the highest requirements of security, speed, and maintainability.

Zero-Leakage Security

Private network hosting inside your VPC. Models are accessed securely without sending telemetry or using your data for foundation model training.

Sub-Second Latency

Optimal semantic caching, edge computing endpoints, and direct streaming connections to API routers ensure zero user friction.

Model Agnosticism

We develop unified orchestration gateways, allowing you to easily swap foundational models (e.g. Claude to GPT or Llama) as performance changes.

FAQ

Frequently Asked Questions

Common inquiries from CTOs and product founders about our technology stack and integration standards.

We analyze your specific accuracy requirements, speed/latency targets, and budget. For reasoning-heavy tasks (like coding assistance or complex math), we utilize Claude 3.5 Sonnet or GPT-4o. For high-volume, low-cost operations, we deploy smaller open-source models (like Llama 3) via private APIs or cloud gateways.

Security is our highest priority. We configure direct API integrations that carry zero-data-retention (ZDR) guarantees—meaning model providers (like OpenAI or Anthropic) cannot train their foundational models on your queries or corporate data. For strict environments, we host models privately inside isolated VPC perimeters.

Yes. We construct all AI applications with model-agnostic orchestration layers (using gateways like LangChain/LangGraph or custom routers). This lets us swap foundation models (e.g. from Claude to GPT or to an open-source model) via a single environment configuration change without altering your core application logic.

Yes. For highly regulated industries (healthtech, fintech), we deploy open-source models (like Llama 3 or Mixtral) inside your private cloud network using Amazon Bedrock or Google Vertex AI. This keeps the data processing loop 100% internal and eliminates external API dependencies.

We match vector storage to your application scale. We commonly deploy Pinecone or Qdrant for dedicated serverless vector storage, Milvus for high-volume local hosting, and PGVector inside PostgreSQL databases (such as Supabase) for integrated relational-semantic setups.