BUILD AI New

Ship Production AI
Features. Not Notebooks.

Pre-vetted AI engineers + managed infrastructure for RAG, fine-tuning, evaluation and inference. Land production AI in weeks, not quarters — without rebuilding the plumbing every project.

Start Building

See the Platform

2–8wk Notebook to production

40+ Senior AI engineers

312K/wk Requests served on platform

99.94% Pipeline uptime

🧠 ai-pipeline.yaml prompt.md eval.json

Production · v3.2 · healthy

Pipeline

📥

Ingest 2.4M docs · 14 sources

↓

🧬

Embed & Index pgvector · 1.7M vectors

98%

↓

⚙️

Retrieve + Generate Claude Sonnet · top-k 12

740ms

↓

🎯

Evaluate LLM-as-judge · 12 evals

94.2

Config

1pipeline: customer-support-rag
2retrieval:
3  store: pgvector
4  top_k: 12
5generation:
6  model: claude-sonnet
7  fallback: gpt-4o-mini
8eval:
9  block_below: 85
10guardrails: [pii_redact, jailbreak]

📈

Eval +3.4 pts prompt v18 shipped

Live

💸

−42% token cost after model routing

Saved

WHY BUILD AI EXISTS

Building AI Features Is Easy.
Shipping Them Is Brutal.

Your team can wire up an LLM API call in an afternoon. Getting that demo to handle a hundred edge cases, run within latency budget, stay within token budget, and not hallucinate when a customer asks an off-script question — that's where most AI roadmaps stall.

Build AI bundles the two missing pieces: senior AI engineers who've shipped this before, and managed infrastructure for the parts you don't want to re-invent (vector storage, eval harnesses, observability, prompt versioning, cost monitoring).

From prototype to production AI in 2–8 weeks
Provider-agnostic — OpenAI, Anthropic, open-source, your choice
Eval, monitoring, and cost controls baked in, not retrofitted

Abstract AI neural network visualization

WHAT BUILD AI INCLUDES

Engineers, Infrastructure, & The Glue Between.

Three layers — staffed and managed together so nothing falls between the cracks.

RAG-as-a-Service

Managed ingestion, chunking, embeddings, vector storage, and hybrid retrieval — across pgvector, Pinecone, Weaviate, Qdrant.

Fine-Tuning Pipeline

Dataset curation, LoRA / full fine-tuning, eval-guarded training runs, and model registry — for open-source and proprietary models.

Evaluation Harness

LLM-as-judge, rubric grading, regression tests, A/B testing — wired into your CI so prompts ship like code.

Observability & Cost

Per-request latency, token usage, model attribution, cost drift alerts — surfaced to product and finance both.

Prompt Versioning

Prompts as code — versioned, reviewed, rolled back if an eval regresses. No more "who changed the prompt at 2am?"

Embedded AI Engineers

Senior AI engineers who've shipped LLM products before — embedded in your team, owning the build end-to-end.

Provider-Agnostic Routing

Switch between OpenAI, Anthropic, Google, and open-source models per route — without rewrites. Optimize cost vs quality.

Safety & Guardrails

PII redaction, jailbreak filters, content moderation, refusal rules — configurable per route, audited per request.

Self-Hostable

Run the entire stack in your VPC if data residency or compliance requires it. SOC 2 + ISO 27001 aligned.

USE CASES

What Teams Ship With Build AI.

💬

RAG Chat / Copilots

Knowledge-grounded chat for support, sales, internal Q&A — with citations, eval, and cost controls from day one.

🔎

Semantic Search

Replace keyword search with intent-aware retrieval across documents, products, code, or tickets.

📑

Document Extraction

Structured data out of invoices, contracts, forms, and PDFs — with confidence scoring and human-in-the-loop fallback.

🤖

Agents & Tool Use

Multi-step agentic workflows with deterministic guardrails — for ops automation, ticket routing, or workflow generation.

🎓

Fine-Tuned Domain Models

Take an open-source base model, fine-tune on your domain, ship behind your existing API. We own the training pipeline.

🧪

Eval & QA Pipelines

If you already have AI in production but no rigorous eval, we drop in a harness that runs in CI and catches regressions.

HOW IT WORKS

From Idea to Prod — in Four Weeks.

Discovery Sprint

One paid week: AI engineer maps your use case, picks a stack, and proposes a scoped pilot with measurable success criteria.

Prototype + Eval

Weeks 2–3: working pipeline running on managed infra. Eval harness wired in. Cost & latency budgets locked.

Production Hardening

Week 4: guardrails, observability, fallbacks, error handling, A/B routing. Ready to take real traffic.

Ongoing Operation

Embedded AI engineer continues to own evals, prompts, and improvements — or hand over to your team with a documented playbook.

MODELS & STACK

Pick Your Stack. Or Let Us Pick One That Fits.

We don't pre-commit you to one provider or framework. The right tool depends on the workload.

LLM Providers

OpenAI Anthropic Google Gemini Mistral Llama DeepSeek

Vector & Retrieval

pgvector Pinecone Weaviate Qdrant Elasticsearch Hybrid BM25

Orchestration

LangGraph LlamaIndex DSPy Custom Temporal n8n

Eval & Observability

LangSmith Braintrust Phoenix OpenTelemetry Datadog

Training & Serving

PyTorch HuggingFace vLLM Modal SageMaker Replicate

Deployment

AWS GCP Azure Kubernetes Self-host (VPC)

Circuit board representing AI infrastructure

WHY REMOTEENGINE BUILD AI

Built by People Who've Already Shipped This.

Plenty of consultancies will sell you an AI workshop. We staff the build with engineers who've personally shipped LLM products at scale — and back the build with infrastructure we maintain, not slideware we'll set up later.

🎯

Outcome, Not Hours

Engagements are scoped to a working production feature — not "we'll bill weekly until you say stop."

⚙️

Infra Included

RAG, eval, observability, prompt versioning — managed infrastructure, not a separate vendor procurement cycle.

🛡️

Production from Day One

Eval, monitoring, and guardrails are baked into v1 — not retrofitted after the demo wins applause and breaks in prod.

🔁

Built to Hand Off

Every pipeline ships with runbooks, eval suites, and decision logs — so your team can own it after we leave.

Talk to an AI Architect

FAQ'S

Build AI Questions

What founders and engineering leaders ask before they commit a quarter to AI.

Book a Scoping Call →

Is Build AI a product, a service, or both?

Both. The platform (managed RAG, eval, observability, prompt versioning) is the product. AI engineers who use the platform to ship features inside your team are the service. Most customers buy them together; you can also buy just one.

Do I have to use a specific LLM provider?

No. Build AI is provider-agnostic — you can route per-feature between OpenAI, Anthropic, open-source, or your own fine-tunes. We help you pick based on cost, latency, and eval scores.

Can the AI engineers work inside our existing codebase?

Yes. The default model is embedded engineering — your repo, your CI, your conventions. The Build AI infrastructure runs in your cloud (or ours) and exposes clean APIs.

What about data residency and compliance?

Self-hostable in your VPC if needed. We're SOC 2 Type II and ISO 27001 aligned. We can also restrict which providers handle which data per route to satisfy regional rules.

How do you price this?

Two components: a flat monthly platform fee (RAG / eval / observability infrastructure) starting at $1,500/mo, plus AI engineer time billed under our standard talent rates. Discovery is a flat $5,000 paid sprint credited against engagement if you proceed.

What happens to the work if we stop the engagement?

You own all code, prompts, eval suites, and fine-tuned models. The platform can be self-hosted or migrated to your stack. We do clean handovers; no vendor lock-in.

Ship Production AI Features. Not Notebooks.