AI & ML Development

Build any AI agent.
Ship it to production.

We design, train, and deploy custom AI systems - from domain-specific agents to fine-tuned models and RAG pipelines. We don't do demos. We do production.

Start a conversation See our agents
Models: GPT-4o · Claude · LlamaFrameworks: LangChain · LlamaIndexInfra: AWS · Azure · GCP
Capabilities

What we build.

End-to-end AI engineering - from architecture through evals and production hardening.

Custom AI agents

Autonomous agents that reason, use tools, and operate within your domain's rules. Memory, multi-step planning, and guardrails built in from day one.

  • Tool use
  • Multi-step reasoning
  • Memory & context

RAG pipelines

Retrieval-augmented generation grounded in your data. We engineer the chunking, embedding, retrieval, and re-ranking so answers are accurate and auditable.

  • Vector search
  • Re-ranking
  • Citation tracking

Model fine-tuning

Task-specific fine-tuning on open-weight models. We handle data curation, training runs, RLHF loops, and distillation so your model fits your problem - not the other way around.

  • LoRA / QLoRA
  • RLHF
  • Distillation

Evals & observability

You can't improve what you don't measure. We build eval harnesses, hallucination metrics, latency dashboards, and regression suites before we hand anything over.

  • LLM-as-judge
  • Tracing
  • Regression tests

Compliance-ready AI

HIPAA, SOC 2, GDPR - we've shipped AI inside regulated industries. Audit logs, data residency, human-in-the-loop checkpoints, and red-teaming included.

  • HIPAA
  • SOC 2
  • Human review loops

Inference & deployment

Self-hosted or managed - we package models as production APIs with autoscaling, cost controls, caching layers, and fallback logic. No science projects.

  • vLLM
  • TGI
  • Batch + streaming
Custom agents we've built

Industry-specific AI, shipped.

Three examples of agents we've designed, trained, and deployed. Different domains - same standard: production-grade, measurable, maintainable.

PDF · DOCX · Scanned docs
Contract Analysis Agent
Risk flags · Q&A · Summary
Clause extraction
Cited sources
Playbook matching
Legal · ComplianceDocument Intelligence Agent

AI agent that reads, analyzes, and answers questions about contracts - with citations.

Manual contract review consumed dozens of hours per week, missed critical clauses, and produced inconsistent risk assessments. We built an agent that ingests any document format, extracts key clauses, flags risks against a company playbook, and answers questions with paragraph-level citations. Handles 500+ page documents.

−70%Contract review time
95%Non-standard clause precision
500+Page documents handled
Claude (long ctx)LlamaIndexpgvectorOCR pipelineNext.js
Read full case study →
Company list (CSV)
Contact Research Agent
Verified contacts + confidence scores
Org mapping
Email validation
Multi-source verify
B2B Sales · BD · RecruitingAutomated Research Agent

Finds the right decision-makers and contact info at any company - at scale.

Sales teams were spending 60% of their time on contact research instead of selling. We built a multi-step agent that maps org structures, identifies key personnel by role, validates contact info across multiple sources, and delivers confidence-scored results. Processes 100+ companies per hour.

30sPer prospect (was 15 min)
3×Qualified outreach volume
85%Contact validation accuracy
Claude / GPT-4FirecrawlApifySupabaseNext.js
Read full case study →
Stack

The tools we reach for.

Narrow fluency in the tools that matter in production, not in demos.

Foundation models

GPT-4oClaude 3.5Llama 3GeminiMistralQwen

Frameworks

LangChainLlamaIndexLangGraphCrewAIDSPyHaystack

Training & fine-tuning

PyTorchHugging FaceAxolotlUnslothTRL

Vector & retrieval

pgvectorPineconeWeaviateQdrantElasticsearch

Observability

LangSmithArizeHeliconeOpenTelemetry

Inference

vLLMTGIOllamaAWS BedrockAzure OpenAI
Process

How we work.

AI projects fail when the model is the last thing built. We flip the order.

01

Problem framing

Before any model is touched, we define success metrics, map data availability, and identify where human judgment must remain in the loop. Deliverable: a written AI brief.

02

Data audit & pipeline

We inventory what data you have, what's missing, and what needs cleaning. Then we build the ingestion, chunking, and embedding pipeline - the part most teams skip.

03

Prototype & eval baseline

A working prototype with an eval harness. We set baseline metrics before we optimize anything - so improvements are real, not anecdotal.

04

Iteration & hardening

Prompt engineering, RAG tuning, or full fine-tuning - whichever moves the eval metrics. Every iteration is measured against the baseline.

05

Production deploy

Containerized inference, cost monitoring, fallback logic, and a human-review interface if the use case requires it. We hand over a system, not a notebook.

Have an AI problem worth solving?

We'll tell you honestly whether it needs a model, an agent, or just a well-written SQL query.

hello@apyx.dev →