AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data rooms
DigitalSign.coAI-designed digital signage for any screen
cstm.aiCustom AI hardware, software, and engineers12+
Years building software
Founded 2013
50+
Production AI deployments
LLM, RAG and agent systems
8+
Vector DBs in production
pgvector to Milvus
24/7
Observability & on-call
Traces, evals, alerts
Core capabilities
Six pillars of modern AI engineering.
LLM Applications
Customer-facing chatbots, copilots, content tools and assistants — built on the right model for the job (GPT, Claude, Gemini, Llama).
RAG & Knowledge Systems
Connect LLMs to your data — retrieval-augmented generation with hybrid search, reranking, and citation-grade answers.
AI Agents & Automation
Multi-step agents that browse, code, call tools and complete real work — LangGraph, CrewAI, AutoGen, custom orchestration.
Private & On-Prem LLMs
Self-hosted Llama/Mistral/Qwen on your infrastructure. Full data control, no third-party API spend, SOC2-friendly.
MLOps & Evaluation
Continuous evals, observability, guardrails, prompt versioning and CI for AI systems — so models stay reliable in production.
Computer Vision & Multimodal
Image, video and document intelligence. Detection, segmentation, OCR, generative imagery, vision-language models.
The full stack
Every layer of an AI system.
Modern AI is rarely one model — it's models, retrieval, evals, infra, guardrails, and UX working together. We build all of it.
Generative AI & LLMs
- Custom LLM application development
- Vibe coding — AI-augmented software delivery
- Prompt engineering & optimization
- LLM fine-tuning (LoRA, QLoRA, full SFT)
- RLHF / DPO alignment
- Custom GPTs and OpenAI Assistants
- Multimodal apps (text + vision + audio)
- Long-context document processing
Retrieval & Knowledge
- RAG pipeline architecture
- Vector database setup — Pinecone, Weaviate, Qdrant, Chroma, Milvus, pgvector
- Embeddings & semantic search
- Hybrid search (BM25 + dense)
- Reranking with cross-encoders
- Knowledge base & knowledge graph integration
- Document extraction & OCR pipelines
- Citation-grounded answer systems
AI Agents & Workflow
- LangChain & LangGraph agents
- CrewAI / AutoGen multi-agent systems
- MCP server development (Model Context Protocol)
- WebMCP browser tool layers
- Tool use & function calling
- Browser-using agents
- Voice agents (LiveKit, Vapi, Retell)
- Customer support copilots
- Sales & RevOps automation
- Workflow automation (n8n, Temporal, Inngest)
Private & Self-Hosted AI
- Custom & private LLM development services
- On-prem Llama, Mistral, Qwen, DeepSeek deployments
- Air-gapped & SOC2/HIPAA-friendly setups
- vLLM, TGI, llama.cpp, Ollama serving
- GPU infrastructure (H100, A100, L40S)
- Model quantization (GPTQ, AWQ, GGUF)
- Inference optimization (TensorRT-LLM)
- Multi-tenant model gateways
- BYOC private cloud LLMs
MLOps & Production
- LLM observability (Langfuse, Arize, LangSmith)
- Continuous evaluation harnesses
- Guardrails & safety filters
- Prompt versioning & A/B testing
- AI gateways (LiteLLM, Portkey, custom)
- Cost & latency monitoring
- Red-teaming & adversarial testing
- CI/CD for AI systems
AI by business function
AI by industry
Computer Vision
- Object detection & segmentation
- OCR & document AI
- Video understanding & action recognition
- Generative imagery (SDXL, Flux, Imagen)
- Vision-language models (CLIP, BLIP, Florence)
- Pose estimation & tracking
- 3D reconstruction & NeRFs
- Edge vision deployments
NLP, Speech & Audio
- Sentiment & intent classification
- Named entity recognition
- Summarization & translation
- Speech-to-text (Whisper, Deepgram)
- Text-to-speech (ElevenLabs, OpenAI TTS)
- Speaker diarization
- Real-time audio pipelines
- Conversational search
Data, ML & Strategy
- Synthetic data generation
- Recommendation engines
- Forecasting & time-series
- Anomaly & fraud detection
- Classical ML & gradient boosting
- AI readiness audits
- Model selection consulting
- AI strategy & roadmap
How we work
From spike to scale in six steps.
- 01
Discover
Workshops, data audits, model selection, ROI sizing.
- 02
Design
System architecture, eval criteria, guardrails, scope.
- 03
Prototype
Working spike on real data in 2–4 weeks.
- 04
Evaluate
Golden datasets, A/B testing, human-in-the-loop QA.
- 05
Deploy
Production rollout with observability and rollbacks.
- 06
Scale
Optimization, fine-tuning, expansion to new use cases.
Tools & models
Fluent in the modern AI stack.
We pick the right tool — not the trendy one. Below is the slice of the ecosystem we deploy most often.
Foundation Models
Frameworks
Vector & Search
Serving & Infra
Cloud AI
Observability
Where we deploy
AI shipped across regulated industries.
Healthcare
Clinical documentation copilots, prior auth automation, HIPAA-grade chat.
Financial Services
Document intelligence, KYC/AML automation, advisor copilots.
Legal
Contract review, discovery, citation-grounded research assistants.
E-Commerce
Product search, generative merchandising, AI customer support.
SaaS
Embedded copilots, AI-native onboarding, in-app agents.
Manufacturing
Vision QA, predictive maintenance, RAG for technical manuals.
Education
Personalized tutors, content generation, assessment automation.
Media
Generative imagery, video tooling, content moderation at scale.
FAQ
Questions clients ask us.
It's the discipline of building software hand-in-glove with AI — pairing engineers with code-generation models, agentic tooling, and rapid iteration loops to ship features dramatically faster than traditional development.
Hosted APIs (OpenAI, Anthropic) win on capability and speed-to-launch. Private LLMs (Llama, Mistral) win on data sovereignty, unit cost at high volume, and predictable latency. We help you pick the right call — and many production systems use both.
A working prototype on real data: 2–4 weeks. Production-ready with evals, reranking, and observability: 6–12 weeks. Most of the work is data prep and eval — not the LLM itself.
Yes — LoRA/QLoRA for adapting open models, full SFT when needed, and DPO/RLHF alignment. We also tell clients when fine-tuning isn't the right answer (usually: try a better prompt or RAG first).
Depends on scale, latency, and existing stack. pgvector is great when you're already on Postgres. Qdrant and Weaviate excel for dedicated workloads. Pinecone for fully managed. Milvus for very large scale. We benchmark for your case.
Yes — but agent reliability comes from constrained tool use, evals, and human-in-the-loop fallbacks, not from longer prompts. We design agents for measurable success rates, not demos.