Vectorphile designs, fine-tunes and operates open-source intelligence — sovereign language models, embedding pipelines and autonomous agent systems, deployed on your infrastructure. No black boxes. No vendor lock-in. Full weight control.
Every model we ship is derived from the strongest open-source foundations, distilled to your domain, quantized for your hardware, and served with your data never leaving your perimeter.
Domain-adapted reasoning model built on open Llama-class weights, QLoRA-fine-tuned on your corpus, instruction-aligned with DPO.
Fine-tuned multilingual embedding model with Matryoshka truncation, contrastive-tuned on your knowledge base for retrieval that actually ranks.
Multi-agent runtime with planner–executor–critic loops, durable memory, tool sandboxing and human-in-the-loop checkpoints. Agents that finish jobs.
From a single embedding pipeline to full multi-agent platforms — we architect, train, evaluate and operate open-source AI systems in production.
Domain-adapted open-source language models — fine-tuned with QLoRA, aligned via DPO, quantized to GGUF/AWQ and served at production scale on your own hardware. From 3B edge models to 70B clusters.
Custom embedding pipelines, hybrid sparse+dense retrieval, and reranking architectures that make proprietary knowledge instantly queryable — with citations your legal team will actually accept.
Multi-agent orchestration with planner–executor–critic loops, tool use, durable memory, and sandboxed code execution. Agents that execute entire workflows — not just chat about them.
Rigorous evaluation harnesses, hallucination detection, faithfulness scoring, and full OpenTelemetry tracing. You don't ship what you can't measure — we make models measurable.
GPU clusters, inference optimization, Kubernetes orchestration, autoscaling and cost engineering purpose-built for LLM workloads. Maximum tokens per dollar, guaranteed by design.
Open-source vs. build-vs-buy analysis, model selection frameworks, and compliance roadmaps that keep you sovereign, secure, and ahead of regulation — GDPR, HIPAA, and beyond.
The Vectorphile inference stack — data to deployment, fully in the open.
Battle-tested tooling across the entire GenAI lifecycle — every layer inspectable, forkable, yours.
A ruthless, engineering-first process. Every phase ships something runnable — no slideware, no vapor, no "Phase 3: magic."
Begin Phase 01We map your data estate, workflows, latency budgets and compliance constraints. Output: a ruthless feasibility report, model shortlist, and a cost model with real GPU pricing — not guesses.
A working vertical slice against your real data: real retrieval, real latency numbers, real failure modes exposed early. If the slice doesn't impress, we don't scale it.
QLoRA fine-tuning, quantization sweeps, DPO alignment, and eval loops until the metrics — not the vibes — say go. Everything tracked, versioned, and reproducible.
Production deployment on Kubernetes, full observability, guardrails, drift detection and on-call support. We stay until the system is boring — boring is the goal.
Tell us what you're building. We'll tell you how fast it can run — and exactly what it costs to keep running.
