DEV.co
AI Development Services

We build production AI that actually ships.

From RAG pipelines and private LLMs to autonomous agents and vibe-coded delivery — DEV.co builds the full modern AI stack. Reliable, observable, and measurably better than the demo.

12+
years building software
50+
production AI deployments
8+
vector DBs in production
24/7
observability & on-call
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 graph integration
  • Document extraction & OCR pipelines
  • Citation-grounded answer systems

AI Agents & Workflow

  • LangChain & LangGraph agents
  • CrewAI / AutoGen multi-agent systems
  • 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

  • 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

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
OpenAIAnthropic ClaudeGoogle GeminiMeta LlamaMistralDeepSeekQwenCohere
Frameworks
LangChainLangGraphLlamaIndexHaystackCrewAIAutoGenDSPyPydantic AI
Vector & Search
PineconeWeaviateQdrantChromaMilvuspgvectorElasticsearchTypesense
Serving & Infra
vLLMTGIOllamaTritonBentoMLRay ServeModalRunPod
Cloud AI
AWS BedrockAzure OpenAIGCP Vertex AICloudflare AIHugging FaceReplicateTogether AIGroq
Observability
LangfuseLangSmithArizeWeights & BiasesHeliconePhoenixPortkeyLiteLLM
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.

What does vibe coding actually mean?
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.
Should we use a private LLM or a hosted API?
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.
How long does a RAG implementation take?
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.
Do you handle fine-tuning?
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).
Which vector database should we use?
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.
Can you build AI agents that actually work?
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.

Have an AI project? Let's build it.

Free 30-minute scoping call. We'll review your use case, recommend a stack, and outline a realistic plan to ship.