Technologies
We work across the full AI ecosystem — not locked to any vendor. We select the right tools for your use case, budget, and risk profile, and we have shipped production systems on all of them.
We design architectures that can swap models and providers as the market evolves. Your AI strategy should not be held hostage by a single vendor's pricing or capability decisions.
Every technology we list here we have run in production — not just in demos. We understand failure modes, scaling behavior, cost curves, and integration edge cases.
GPT-4o, Claude, and Gemini all have different strengths. We benchmark on your actual data and select based on accuracy, latency, cost, and compliance fit — not hype.
We work at the API level, fine-tuning level, and inference infrastructure level across all major frontier and open-weight models.
The most widely adopted frontier models with best-in-class function calling, tool use, reasoning, and multimodal capabilities.
Leading model for long-context reasoning, document analysis, agentic coding, and safety-critical enterprise deployments.
Best-in-class multimodal, million-token context windows, and native GCP integration for enterprises in the Google ecosystem.
Self-hosted, fine-tunable models for data-sovereignty requirements, cost optimization, and regulated industries.
Managed model access for AWS-native enterprises with VPC isolation, CloudTrail logging, and IAM-based access control.
Emerging frontier and purpose-built models for code, reasoning, biology, finance, and vision with higher accuracy on domain-specific tasks.
Raw models don't ship. We select and customize the right orchestration framework for your workflow complexity, latency requirements, and team capabilities.
The most adopted OSS framework for RAG pipelines, chains, and stateful multi-agent graphs. LangGraph adds structured execution with branching and persistence.
OpenAI's production agent SDK with built-in handoffs, guardrails, tool use, and tracing. The reference implementation for OpenAI-based agentic systems.
Role-based multi-agent orchestration with built-in collaboration patterns. Ideal for complex workflows requiring specialized agents working in coordinated crews.
Microsoft's framework for conversational multi-agent systems. Excellent for back-and-forth agent dialogue, code execution agents, and human-in-the-loop patterns.
Google's Agent Development Kit for building production-grade multi-agent systems on Vertex AI. Native integration with Gemini, Workspace, and GCP services.
Anthropic's open standard for connecting AI models to tools, data sources, and enterprise systems. Enables composable, auditable agent-tool interactions at scale.
Best-in-class data indexing and RAG primitives. Pydantic AI for type-safe agent construction. DSPy and Instructor for structured, optimizable LLM programs.
We design data architectures that make enterprise data AI-ready — clean, structured, retrievable, and governed. Vector databases, data warehouses, knowledge graphs, and streaming pipelines.
Vector Databases
Data Warehouses
Document & Graph
ETL & Ingestion
Model deployment, observability, cost control, and CI/CD for AI pipelines. We build infrastructure that treats AI systems with the same rigor as any mission-critical application.
Enterprise AI security spans models, prompts, retrieval systems, agents, tools, datasets, cloud boundaries, and human approvals. We combine proven cybersecurity controls with AI-specific frameworks and testing methods.
Frameworks & Threat Models
Structured baselines for AI risk, control design, security review, and regulatory readiness.
Guardrails & Runtime Controls
Input, output, retrieval, and tool-use controls that reduce injection, leakage, unsafe actions, and over-permissive agents.
Red Teaming & Evaluation
Adversarial testing for prompt injection, jailbreaking, tool abuse, data exposure, unsafe outputs, and reliability failures.
Data Protection
Controls for PII, sensitive enterprise data, retrieval exposure, retention, anonymization, and cross-tenant isolation.
Cloud, Identity & Secrets
Enterprise-grade isolation and identity controls around model access, tools, data planes, and agent execution.
Supply Chain & Operations
Controls for model artifacts, prompts, datasets, dependencies, deployment pipelines, audit trails, and incident response.
We benchmark AI workloads across accelerator options and work with silicon vendors to validate platforms against real enterprise AI requirements — from inference cost modeling to private model training on custom silicon.
The dominant GPU platform for AI training and inference. We size, benchmark, and optimize workloads across the full Hopper and Blackwell lineup.
High-memory bandwidth accelerators for LLM inference and training. Strong open-source toolchain via ROCm, increasingly adopted in large-scale AI clusters.
Purpose-built AWS chips optimized for cost-efficient training and inference at scale within the AWS ecosystem.
Google's custom AI accelerators — the foundation of Gemini training. Available via Vertex AI and Cloud TPU for large-scale distributed training workloads.
Intel's Gaudi accelerators and Xeon platforms for cost-effective inference and on-premises AI deployment with strong enterprise support and open toolchains.
Ultra-low latency inference silicon for on-device, edge, and deterministic-throughput deployments where cloud round-trips are not viable.
AI systems don't exist in isolation. We integrate with the enterprise applications your teams already use every day.
CRM & Sales
Collaboration
ERP & ITSM
Support
We run rapid technology assessments to match the right tools to your requirements — not the other way around. No vendor bias. No lock-in.
pgvector
Redis Search
OpenSearch
Snowflake
BigQuery
Databricks
Elasticsearch
MongoDB
Neo4j
Apache Kafka
Apache Flink
Airbyte
Apache Spark
Vertex AI
Docker
Kubernetes
Terraform
GitHub Actions
HubSpot
Notion
Confluence
SAP
Jira
Zendesk
Intercom