Accepting Q4 AI builds · 10-day startDoc. AI / Overview

AI development services that survive production

01Evals before launch
02Your models, your data
03US-based senior team
04Fixed-price scopes

DEV.co provides AI development services for companies that need AI working inside real systems, not a demo. We build and operate LLM applications, retrieval systems, AI agents, and private models, with evals and guardrails in the first release and every line of code owned by you.

Scope your AI project See our work

Fig. 01 / Trace of one production request1.34 s · 8 spans

gateway.auth
40 ms
pii.redact
30 ms
retrieve.hybrid
190 ms
tool.crm_lookup
310 ms
rerank
80 ms
model.generate
760 ms
guardrail.policy
70 ms
eval.online
90 ms

Illustrative. The model call is the longest span and the least of the work.

Software and AI delivered for

12+

Years building software

Founded 2013

50+

Production AI deployments

LLM, RAG, and agent systems

Vector databases in production

pgvector to Milvus

24/7

Observability and on-call

Traces, evals, alerts

AI development services

Eight AI development services, one accountable team

Most AI projects need several at once, and failures happen at the seams. We staff the whole path from data to deployment.

LLM application development

Copilots and assistants on the right model for the job: GPT, Claude, Gemini, or Llama.

/ai/custom-llm-apps 02

RAG and knowledge systems

Search over your documents, with answers that cite their source.

/ai/rag 03

AI agent development

Agents that call tools, update records, and hand off to a person when unsure.

/ai/agent-development 04

MCP server development

Scoped, audited access to your systems for any agent.

/ai/mcp 05

Private and on-prem LLMs

Open models on your own infrastructure, for data that cannot leave.

/ai/private-llm 06

Document intelligence

OCR and extraction that turn PDFs and scans into records.

/ai/document-intelligence 07

Vector database engineering

pgvector to Milvus, benchmarked and tuned for your workload.

/ai/vector-database 08

Coding agents and vibe coding

AI-assisted delivery for your engineers, with review gates.

/ai/coding-agents

Anatomy of an AI system

The model is the smallest part of the system

A production AI feature is a request path, not a prompt: inputs scrubbed, context retrieved, tools scoped, and every answer checked before anyone sees it.

Fig. 02 / Reference AI systemRev. 2026.Q4

INPUTSUsers (Web · mobile · Slack), Agents (MCP callers), Events (Webhooks · queues), Documents (Ingest pipeline)

GATEWAYAuth, PII redaction, rate limits

ORCHESTRATIONRouter (Model selection), Retrieval (Hybrid search · rerank), Tools (MCP · function calls), Guardrails (Policy · schema checks)

MODELSHosted API (Claude · GPT · Gemini), Private LLM (Llama · Qwen on vLLM), Embeddings (Text · vision), Fine-tuned (LoRA adapters)

PLATFORM — SHIPPED WITH EVERY BUILDEvals (Golden set · CI gate), Observability (Traces · cost · latency), Prompt registry (Versioned · A/B), Security (SSO · audit log)

A reference build. The platform row ships with every engagement.

Evaluation

Measured before it ships, and every week after

The gap between an AI demo and an AI product is evidence. Before launch we build a golden dataset from your real cases and score every change against it.

A prompt edit or model upgrade has to beat the current release before it merges, so our AI development services move fast without breaking what works.

Golden datasets reviewed by your own experts
Automated scoring for faithfulness and accuracy
Human review where a metric cannot judge
Latency and cost budgets enforced too

Fig. 03 / Eval reportIllustrative · golden set

Launch gate 0.90

0.71v1

0.78v2

0.84v3

0.88v4

0.92v5

0.94v6

Faithfulness0.94PASS
Answer accuracy0.91PASS
Citation coverage0.97PASS
Correct refusals0.96PASS
p95 latency1.4 sPASS

How we work

From working prototype to production in twelve weeks

Every two weeks there is a working build on real data, and an eval score that says whether it got better.

PhaseTwelve-week delivery timeline

Discover

Wk 1–2

Data audit, model shortlist, and the success metric.

Prototype

Wk 2–4

A working spike on your real data.

Evaluate

Wk 3–6

A golden dataset and the baseline every change must beat.

Build

Wk 4–10

Retrieval, tools, guardrails, and UI in two-week increments.

Launch

Wk 10–12

Staged rollout, tracing, rollback plan, runbook.

Operate

Ongoing

Weekly evals, model upgrades, cost tuning.

Model strategy

Hosted API, private LLM, or a fine-tuned model

An honest read. Many production systems use more than one.

Criterion	Hosted API	Private LLM	Fine-tuned open model
Time to first build	Days	2–4 weeks	4–8 weeks
Capability	Frontier	Strong, a step behind	High on one narrow task
Data leaves your network	Yes, under vendor terms	No	No, once trained
Cost at high volume	Grows with every call	Fixed by hardware	Lowest per call
Best when	Speed and capability matter most	Data is regulated or sensitive	One task runs at very high volume

Regulated data points to a private LLM deployment; most teams should start on a hosted API.

Technology

Fluent in the modern AI stack

Chosen for your constraints and for whoever maintains it after us.

Foundation models

OpenAI/Anthropic Claude/Google Gemini/Meta Llama/Mistral/DeepSeek/Qwen/Cohere

Frameworks

LangChain/LangGraph/LlamaIndex/Haystack/CrewAI/AutoGen/DSPy/Pydantic AI

Vector and search

Pinecone/Weaviate/Qdrant/Chroma/Milvus/pgvector/Elasticsearch/Typesense

Serving and infra

vLLM/TGI/Ollama/Triton/BentoML/Ray Serve/Modal/RunPod

Cloud AI

AWS Bedrock/Azure OpenAI/GCP Vertex AI/Cloudflare AI/Hugging Face/Together AI/Groq

Observability

Langfuse/LangSmith/Arize/Weights & Biases/Helicone/Phoenix/Portkey/LiteLLM

Where it applies

AI development services by function and industry

The engineering is the same; the constraints change.

Why DEV.co

What you get from an AI development company that ships

Six commitments we will put in writing.

P.01

Evals in the contract

Success is a number agreed in week one and reported every sprint.

P.02

You own all of it

Code, prompts, eval sets, and weights are yours from the first commit.

P.03

Model-agnostic

No reseller deals, so the model choice follows your constraints.

P.04

Senior US engineers

The people on the scoping call write the code.

P.05

Secure by default

PII redaction, scoped permissions, and audit logs ship in release one.

P.06

We run AI ourselves

We operate Phony.ai voice agents and LLM.co private models, so we are on call for what we recommend.

Questions

Hiring an AI development partner

What do your AI development services include?

Discovery, data preparation, model selection, application and retrieval engineering, agents, evaluation, security review, deployment, and ongoing operation. Most clients start with a fixed-scope prototype on real data.

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, full SFT, and DPO/RLHF alignment. We also say when fine-tuning isn't the answer; usually a better prompt or RAG comes first.

Which vector database should we use?

pgvector if you already run Postgres, Qdrant or Weaviate for dedicated workloads, Pinecone for fully managed, Milvus at 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.