AI Software Development Services

Repeat questions, documents no one can find, data retyped between systems — we turn those into AI that keeps working in production, well past the demo. A dedicated senior team, with the evaluation and guardrails to prove it.

Problems We Solve

Bluepes builds production AI for mid-market and growth-stage companies: assistants that answer from your own data, agents that finish multi-step tasks, and automation that moves work between systems. A dedicated team of senior engineers delivers in the CET/EET time zone, with evaluation, access control, and observability from the first sprint. We start from the problem you actually have, and we say when AI is not the right tool for it.

Your team answers the same questions all day
- A customer asks something your help center already covers, and an agent still writes the reply by hand. We build an assistant that answers from your own policies and docs, cites the source, and passes anything unclear to a person.
The answer is in a document nobody can find
- The clause exists in a contract and the rule exists in a policy, but locating either takes three messages and a colleague's memory. A retrieval assistant reads your library and answers with a citation to the exact source.
The same data gets retyped between systems
- An order lands in one tool and someone keys it into another because the two do not connect. We automate the hand-off — extract, validate, route — and keep a human check where it matters.

Intake and onboarding wait on manual review
- A claim, an application, or a form sits in a queue until someone reads it and types the fields into a system. Document intelligence pulls the fields, checks them, and moves the case forward.
Your dev team can't ship fast enough
- The backlog grows faster than you can hire. We set up an AI-assisted development workflow inside your team — coding agents, AI review, generated tests — with guardrails your engineers control.
You're not sure AI is the right tool
- There is pressure to add AI, and an equal risk of building the wrong thing. We start with a short proof of concept that says plainly whether AI fits, what it costs to run, and what it takes to build.

What We Build

Agents, RAG, automation, and the engineering that keeps them reliable.
AI Agents and Agentic Workflows (MCP)
Agents that finish multi-step tasks against your tools and data, connected through the Model Context Protocol (MCP). Example: an agent that triages a ticket, pulls the order, drafts a reply, and asks a person to approve the refund.
RAG Systems and Knowledge Assistants
Question-answering over your documents with vector or hybrid search and citations. Example: a policy assistant that answers staff questions from your handbook and links the exact clause.
AI Automation and LLM Integration
AI features inside your existing apps, plus automation across services with n8n. Example: a flow that reads incoming invoices, extracts line items, and posts them to your ERP for approval.
AI-Assisted Development (ADLC)
An AI-assisted workflow set up inside your engineering team. Example: coding agents connected to your repo through MCP, with AI review and generated tests behind guardrails.
Evaluation and LLMOps
The engineering that keeps the above reliable after launch: evaluation sets, guardrails, observability, and cost control.
AI Proof of Concept
A short paid PoC that tests feasibility, data readiness, and run cost before a full build.

When to Use Which Approach

Not every problem needs an agent. A short guide to which approach fits which situation.

When RAG fits
The answer lives in your own documents and that content changes — policies, contracts, product docs, support history. The assistant retrieves and cites, so answers stay current and traceable.
When an AI agent fits
A task runs across several steps and tools — read a record, check a system, draft an action — and you need it carried through to the finished action.
When automation fits
Work moves between systems on a schedule or a trigger — extraction, routing, drafting — and a person only steps in to approve the result.
When prompt-only is enough
A single, low-volume feature — a summary, a classification, a rewrite — with no document base to search and no multi-step action behind it.
When not to use AI
A fixed rule, a report, or a plain integration solves it more cheaply and predictably. We say so during discovery instead of selling a model you do not need.

AI by Industry

Real estate / PropTech
- Listing generation, lease and document extraction, and tenant and agent Q&A.
Telecom
- Support deflection and operations automation over high-volume data.
EdTech
- Tutoring and knowledge assistants, built within FERPA and COPPA limits.

Benefits for Your Business

What changes once it ships.
Hours Back for Your Team
Support answers from the docs, ops stops rekeying data, engineering stops writing boilerplate.
Answers People Trust
Grounded in your data, with a citation to the source you can open and check.
Work Stops Waiting on a Person
Intake, onboarding, and reviews move at the speed the model can check them, clearing the queue instead of waiting in it.
Built Into Your Systems
AI inside your data and permissions, reached through the access rules you already run.
Reliable in Production
Evaluation, guardrails, and observability from the first sprint.

What Makes Us Different

MCP-First Integration
- Agents reach your tools through the Model Context Protocol — one standard interface across the systems they touch.
Shipped on Evaluation
- Every build passes a test set from your real examples before launch.
Reproducible by Design
- Deterministic workflows, so behavior does not drift run to run.

We'll Talk You Out of It
- If a rule or a report solves the problem, we say so in discovery.
Mid-Market Focus
- Sized and priced for growth-stage teams.

How We Work

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.

Technologies We Use

The applied-AI stack we work with.
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

RAG vs Fine-tuning vs Prompt-only

The three common ways to put a language model to work on your data, across the dimensions that decide cost, accuracy, and maintenance.

	RAG	Fine-tuning	Prompt-only
What it does	Retrieves your documents at query time and generates a grounded answer	Adjusts a model's weights on your examples to change behavior or format	Instructs a base model using context placed in the prompt
Best for	Q&A over internal knowledge that changes	Fixed formats, narrow high-volume tasks, tone	Quick features, low volume, simple tasks
Data needed	Your document set, indexed	A large set of labeled examples	Little to none
Keeps answers current	Yes — update the source data	No — retrain to update	Not applicable
Setup effort	Medium	High	Low
Ongoing cost	Retrieval and tokens	Retraining cycles	Tokens
Risk of wrong answers	Lower — grounded and cited	Still invents facts not in the weights	High without grounding
When Bluepes recommends it	Most internal-knowledge and support use cases	Rarely for mid-market; only stable, narrow, high-volume tasks	Prototypes and simple features