Hands-on engineering experience across the foundation model landscape — frontier APIs, open-weight models, fine-tuned variants, and specialty models for vision, speech, and code. We pick the right one for each problem.
Foundation models differ in ways that matter operationally — context length, latency, cost, fine-tuning support, function-calling reliability, and multilingual depth. Picking by reputation alone leaves performance and budget on the table. We benchmark on your data before recommending a model, and we stay current as new releases shift the landscape.
Discuss your project ↗Every engagement gets shaped to fit, but these are the building blocks we rely on.
GPT-4, Claude Sonnet/Opus, Gemini — engineered into production with proper rate-limit handling, fallback chains, and cost controls.
Llama 3, Mistral, Qwen, Gemma — self-hosted via vLLM or Ollama for cost, privacy, or latency reasons. Fine-tuning included.
GPT-4 Vision, Claude Vision, LLaVA, and specialized vision-language models. Image understanding built into product workflows.
Whisper for transcription, ElevenLabs and Cartesia for synthesis, and custom voice models for speech-driven products.
Code Llama, DeepSeek Coder, and frontier models for code generation, review, and developer-tool features.
OpenAI, Cohere, and open-weight embedding models — picked and benchmarked for your retrieval use case rather than chosen by default.
Two decades of engineering practice, sharpened by the realities of production AI.
Public benchmarks don't predict performance on your specific problem. We test multiple models against your actual data before recommending.
We've shipped both. We'll tell you honestly when API costs justify self-hosting and when they don't.
AI moves quickly. We track new model releases, benchmark them, and surface the ones that change recommendations.
Architectures designed so model swaps are afternoons, not quarters. Lock-in to one provider is a risk we engineer against.
Let's discuss how this fits your business. We reply within one working day.
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