Every AI we deploy for clients — and when each one wins.

What it is. Anthropic's frontier LLM family. Strong long-context reasoning, conservative refusals, the cleanest API surface for production RAG.

When to use it. Production RAG + agent backends, code-heavy workflows, longer documents, regulated industries that prefer Anthropic's Constitutional AI approach.

Our experience. The chatbot at /secure-ai-playground on this site runs on Claude Haiku with prompt caching. We deploy Claude via the Anthropic API directly + via Amazon Bedrock for clients in the AWS ecosystem.

• Bedrock deployment for VPC-scoped data residency
• Prompt caching for cost-controlled production workloads
• Anthropic API Workspace + Member API keys for scoped access

What it is. The market leader in consumer + prosumer AI. Best general-purpose chat experience; broadest plugin + custom-GPT ecosystem.

When to use it. Teams already running ChatGPT informally (most are by 2026); the question becomes ChatGPT Enterprise vs Microsoft Copilot. We help that decision land based on your existing M365 vs Workspace footprint.

Our experience. We deploy ChatGPT Enterprise + Team for clients who prefer the OpenAI experience over Copilot. We also deploy Azure OpenAI for clients who want OpenAI models inside their Microsoft tenant boundary.

• ChatGPT Enterprise has SOC 2 Type 2 + zero training-data retention
• Azure OpenAI Service for enterprise-grounded deployments under Microsoft contracts
• Custom GPT governance: SSO + admin-approved GPT registry only

What it is. Google's AI suite. Gemini integrated into Workspace (Gmail, Docs, Sheets, Meet, Slides) + Vertex AI for production LLM workloads.

When to use it. Workspace-native organizations should prefer Gemini for productivity. Vertex AI for GCP-native production AI workloads, especially when the data is already in BigQuery.

Our experience. We deploy Gemini for Workspace as part of our Google Workspace Partner work. Vertex AI deployments are typically tied to clients with significant GCP footprint.

• Workspace data residency controls flow through to Gemini's grounding
• Vertex AI Model Garden + Endpoints with IAM-scoped access
• Google's Responsible AI guardrails inherited

What it is. Research-focused AI with citation-grounded answers. Replaces Google for "answer me, cite your source" workflows.

When to use it. Knowledge workers who do research-heavy work — competitive intelligence, regulatory research, sales research. Pairs well with Copilot for execution AFTER research.

Our experience. We deploy Perplexity Enterprise for sales + research teams as a complement to (not replacement for) the primary AI suite.

• SSO + SCIM provisioning on Enterprise
• No training on enterprise data
• Workspace allowlist controls via your IdP

What it is. Self-hosted open-weight models (Llama, Mistral, Qwen, gpt-oss). The data never leaves your infrastructure.

When to use it. Hard data-residency requirements (clinical PHI, classified contracts, IP-sensitive R&D). Air-gapped environments. Cases where the regulatory + insurance cost of cloud LLM data flow exceeds the operational cost of self-hosting.

Our experience. We deploy local LLM inference for clients in regulated industries who need on-prem AI workflows. Typical stack: Ollama or vLLM on dedicated GPU infrastructure with our managed observability.

• No data leaves the client environment
• GPU capacity planning + monitoring under our SOC
• Air-gapped models for highest-sensitivity workflows

What it is. IDE-embedded AI coding assistant. Code completion, in-editor chat, PR review, security scanning.

When to use it. Engineering teams. Most-validated AI ROI surface in 2026. Enterprise tier adds SAML, audit logs, IP indemnification.

Our experience. We deploy GitHub Copilot Enterprise for our own team and for clients with engineering organizations. Often paired with our broader DevSecOps practice.

• SAML SSO + SCIM provisioning
• Public-code matching filter ON by default
• Audit logs piped to your SIEM
• IP indemnification at Enterprise tier