What are LLM development services?

Building applications on top of large language models — RAG assistants, agents, in-app LLM features, and automation — instead of training models from scratch. The work covers data and retrieval design, integration, evaluation, and production operations, delivered as applied engineering with guardrails.

Do you train your own AI models?

No. Bluepes builds applied AI on top of foundation models. For most mid-market cases, getting a foundation model to work reliably on your data delivers value faster and cheaper than custom training. Where a specialized model is genuinely required, we scope it explicitly.

How much does custom AI development cost?

It depends on scope, data readiness, and integrations. Bluepes starts with a short paid proof of concept that confirms feasibility and run cost, then sizes the build as a dedicated-team engagement rather than a fixed per-feature price.

How do you choose an AI development company?

Look for a team that starts from your problem, works on your data and systems, and ships with evaluation and guardrails rather than a demo. Ask how they measure quality before launch, how they handle your data and permissions, and whether the code and IP are yours. A short paid PoC is the low-risk way to test fit.

Which LLM providers do you work with?

OpenAI, Azure OpenAI / Azure AI Foundry, and open models via Hugging Face. Provider choice depends on your cloud, data-residency needs, latency, and cost per request. Integrations stay provider-aware, so you are not locked to one vendor without reason.

How do you evaluate an LLM system before launch?

We define the behavior the system should produce, build an evaluation set from real examples, and test output against it, then add guardrails for what the model may and may not do. Launch depends on passing that evaluation set.

Generative AI and LLM Development Services

Generative AI & LLM Development Services

Applied AI engineered to production — RAG, agents, and LLM features, with evaluation and guardrails

We engineer applied AI — RAG systems, AI agents, and LLM features — and take them to production with evaluation, guardrails, and observability built in. Delivered by a dedicated senior team.

Why Applied AI, Done Right

Bluepes builds applied AI on top of foundation models instead of training them from scratch. What that gives you:

Built to Run in Production
- Foundation models on your data, taken past the prototype stage to something you operate every day.
Evaluation-First
- A build ships once it clears an evaluation set drawn from your real examples.
Integrated Into Your Systems
- AI wired into your systems and reached through the access rules you already run.

Problems We Solve

Where applied-AI projects break, and what we build to prevent it.

Your AI demo works, then breaks on real inputs
- We build evaluation, guardrails, and logging in from the start, so behavior stays visible and correctable.
Retrieval returns noise, so the assistant is wrong
- We tune ingestion, chunking, and hybrid search to your documents, and validate retrieval before generation.

Automations fail silently
- We add checks, fallbacks, and observability so a broken step raises a flag instead of dropping work.
AI dev tools can't see your code or docs
- We connect them through MCP, inside your permissions, with AI review and generated tests behind guardrails.

Our Approach

Step 1

Discovery and feasibility

We run a short assessment: what the model must do, whether your data supports it, and the cost per request at your volume. The output is a recommended approach — RAG, agent, automation, or prompt-only — plus a clear go/no-go you can act on.

Step 2

Data and retrieval design

We inspect how your documents are structured and match the retrieval method: vector search for meaning, keyword or hybrid search for exact terms and IDs. We validate retrieval quality before generation.

Step 3

Build

We implement the agent, RAG pipeline, or LLM feature with explicit tool boundaries and, where an agent must hold context across steps, structured long-term memory instead of an ever-growing prompt. Everything connects through APIs and MCP, inside your permissions.

Step 4

Evaluation and guardrails

We build an evaluation set from real examples, score output against it, and add guardrails: input validation, allowed actions, and fallbacks. The evaluation stays in place to catch regressions.

Step 5

Deployment and observability

We release with tracing and logging so every retrieval and action is visible, monitor cost and latency, and iterate on prompts, retrieval, and model choice against real usage.

What We Offer

Same capabilities as our AI solutions page, delivered at engineering depth.

RAG Systems
- Ingestion and chunking tuned to your documents, embeddings, vector and hybrid search, multimodal retrieval, grounded answers with citations.
AI Agents and MCP
- Task and workflow agents, MCP tool servers, deterministic workflows, and approval-in-the-loop control.
LLM Integration and Automation
- In-app LLM features and n8n automations connecting internal services, third-party platforms, and AI providers.

AI-Assisted Development (ADLC)
- Coding agents, AI code review and test generation, guardrails and evaluation, set up inside your team.
Evaluation and LLMOps
- Evaluation sets, guardrails, observability, and cost control that keep a system reliable after launch.

Build In-House vs Off-the-Shelf vs Dedicated Team

How building AI in-house, buying an off-the-shelf tool, and a Bluepes dedicated team compare across the dimensions that decide speed, fit, reliability, and cost.

	Build in-house	Off-the-shelf AI tool	Bluepes dedicated team
Time to value	Slow — gated by hiring and ramp-up	Fast to switch on	Live in one to two weeks
Fit to your data and systems	High, if you have the people	Limited to the vendor's assumptions	Built around your data and systems
Control and customization	Full	Low — tied to the vendor roadmap	Full — the code and IP are yours
Evaluation and reliability	Depends on in-house maturity	Vendor-controlled and often opaque	Evaluation and guardrails built in
Ongoing maintenance	Yours to run	Vendor-managed	Handover plus support
Cost model	Salaries and infrastructure	Subscription per seat or usage	A dedicated-team engagement
Best for	Teams with AI engineers to spare	Standard, non-differentiating use	Custom AI that must fit your systems and stay reliable

What Makes Us Different

Agents Connect via MCP
- Agents reach your tools through the Model Context Protocol — one standard interface across every system they touch.
Shipped on a Passing Evaluation Set
- Every build clears a test set drawn from your real examples before it launches.
Reproducible Workflows
- The same input gives the same result, run to run.
Honest Scoping
- We tell you during discovery when AI is the wrong tool, before you spend on it.

Technologies We Use

We work with a modern applied-AI stack:

Languages
- Java, C#/.NET, Node.js/NestJS, TypeScript, Python
LLMs & AI Platforms
- OpenAI, Azure OpenAI / Azure AI Foundry, Hugging Face
Agents & Automation
- Model Context Protocol (MCP), agent orchestration, n8n

Retrieval & Data
- Embeddings, vector search, hybrid search, PostgreSQL / pgvector
Evaluation, Observability & Cloud
- OpenTelemetry, Sentry, Azure, AWS, Docker, CI/CD