Generative AI Development Services

EltexSoft builds generative AI systems that ship to production. RAG pipelines, AI agents, LLM integrations, copilots. Senior engineers. $50-99/hr.

Talk to us

EltexSoft is a boutique generative AI development studio. We build RAG systems, AI agents, and LLM integrations that ship to production, not pilot demos. 11 years in business, 35-50 senior engineers, 3+ year average client engagement. Headquartered in Lisbon, engineering team in Ukraine. $50-99/hr.

What we ship

The Work

95% of Enterprise AI Pilots Fail. We Build the 5% That Works.

MIT Project NANDA studied 300+ enterprise AI deployments in 2025. The finding: 95% delivered no measurable P&L impact. The diagnosis was specific. Most systems don’t retain feedback, don’t adapt to context, and don’t improve over time. They’re demos, not products.

EltexSoft is a boutique engineering studio. 35-50 senior engineers, no junior leverage, no offshore handoff. We’ve been building production software since 2015. The AI layer is new. The engineering discipline is not. When we build a generative AI system, it ships with evaluation harnesses, observability, CI/CD for prompts, and a team that stays to maintain it. Our average client engagement is 3+ years. That’s a retention rate, not a tagline.

Enterprise spending on generative AI hit $37 billion in 2025, tripling from $11.5 billion the year before (Menlo Ventures). Gartner predicts 33% of enterprise software will include agentic AI by 2028. The money is flowing. The question is whether it produces anything.

We think the answer depends on who builds it.

What We Build

Retrieval-Augmented Generation connects an LLM to your data so it answers from facts, not training data. We build the full pipeline: document ingestion, chunking strategies, embedding with OpenAI or Cohere models, vector storage in Pinecone, Qdrant, or pgvector, hybrid retrieval (BM25 + semantic), re-ranking, generation with source citations, and evaluation.

Our RAG systems run in production for clients in LegalTech and FinTech, processing millions of documents with sub-second query latency. The difference between a RAG demo and a RAG product is the evaluation layer. We build that first.

AI Agents and Agentic Workflows

Agents that plan, execute, use tools, and self-correct. We build with LangGraph for complex state machines, CrewAI for role-based multi-agent orchestration, and the OpenAI Agents SDK for function-calling patterns.

Real-world applications we’ve delivered: automated document review pipelines, multi-step research agents, and workflow automation that replaces manual processes costing clients hundreds of engineer-hours per month. Every agent system includes human-in-the-loop checkpoints for high-stakes decisions.

Gartner warns that over 40% of agentic AI projects will be canceled by 2027 due to escalating costs and unclear business value. The ones that survive are scoped tightly, evaluated rigorously, and built by engineers who shipped production software before the AI hype cycle.

LLM Integration Into Existing Products

The fastest path to generative AI ROI is connecting a foundation model to your existing product. We integrate OpenAI, Anthropic Claude, Google Gemini, and open-source models (Llama 4, Mistral) into SaaS platforms, internal tools, and customer-facing applications.

What we add that a raw API call doesn’t give you: prompt management and versioning, response caching, cost controls and token budgeting, fallback routing across providers, and an abstraction layer that lets you swap models without touching application code.

AI Copilots and Chatbots

Production chatbots and copilots for customer support, internal knowledge, sales enablement, and industry-specific workflows. We’ve built copilots for legal document analysis, healthcare intake, and financial compliance review.

The gap between a chatbot demo and a chatbot product is evaluation. Ours ship with golden test datasets, faithfulness scoring, and production monitoring via Langfuse, so you know when the system gives a wrong answer before your customer does.

Intelligent Document Processing

Extraction, summarization, classification, and redaction at scale. We’ve processed insurance claims, legal contracts, medical records, and financial filings. Typical pipeline: OCR or native PDF extraction, entity recognition, structured output, human review queue for edge cases, and continuous learning from corrections.

GenAI Strategy and Audit

For teams that aren’t sure where to start. We evaluate your data, infrastructure, and candidate use cases, then deliver a working prototype on your real data with a written go/no-go business case. A discovery sprint takes 4-8 weeks and costs $25K-$60K. You get a prototype and a decision framework, not a slide deck.

What It Costs

We publish our pricing because serious buyers deserve real numbers.

Discovery sprint: $25K-$60K over 4-8 weeks. You get a working prototype on your data, written success criteria, and a go/no-go recommendation.

MVP build: $80K-$250K over 3-5 months. A production-ready system with evaluation harness, observability, CI/CD, and a runbook. Typical team: AI/ML lead, 1-2 AI engineers, data engineer, QA.

Retained AI engineering team: $40K-$90K per month. A dedicated pod of 4-6 engineers who stay on your project for as long as you need them. This is our core model. Same team, month 1 and month 36.

Staff augmentation: If you have a delivery framework and need specific roles (RAG architect, prompt engineer, LLM evaluation specialist), we embed senior engineers into your team. $50-99/hr.

For context: Clutch’s April 2026 data puts the average AI development project at $120K over 10 months. Senior AI engineers in the US cost $150-$250+/hr with 3-6 month hiring timelines. Our rates are $50-99/hr for engineers with 5-15 years of experience, based in Lisbon and Ukraine.

The Technical Stack

We name the tools because senior engineers scan this section to disqualify vendors who can’t.

Foundation models: OpenAI GPT-5.5 and GPT-5.4, Anthropic Claude Opus 4.7 and Sonnet 4.6, Google Gemini 3.1 Pro and 3 Flash, Meta Llama 4, Mistral, Cohere. Open-source for on-premise and sovereign deployments.

Orchestration: LangChain and LangGraph for complex chains and stateful agents. LlamaIndex for RAG-first architectures. CrewAI for multi-agent systems. OpenAI Agents SDK for function-calling patterns. Anthropic’s Model Context Protocol (MCP) for tool integration.

Vector databases: Pinecone (managed, fastest setup), Qdrant (Rust-based, best performance per dollar), Weaviate (best hybrid search), pgvector (when you’re already on Postgres and under 50M vectors).

Evaluation and observability: Langfuse (26K+ GitHub stars, 50M+ monthly SDK installs, the industry standard), Arize Phoenix, LangSmith, custom evaluation harnesses with LLM-as-judge and golden datasets.

Infrastructure: AWS Bedrock, Azure OpenAI Service, Google Vertex AI, GPU provisioning, MLOps with MLflow and ZenML.

How We Work

Week 1-2: Discovery. We audit your data, define success criteria, and agree on evaluation metrics before writing code. Every failed AI project we’ve seen started without this step.

Week 3-8: Prototype on your real data, not synthetic or demo data. You see results on your use case within the first month.

Month 2-5: Production build. CI/CD for prompts and models. Evaluation suite running on every deployment. Observability from day one. Weekly Friday demos so you see progress every week.

Month 6+: Iteration and maintenance. Models improve. Your data changes. Costs need optimization. We stay. Our average engagement is 3+ years because generative AI systems need ongoing engineering, not a handoff.

Who We Are

EltexSoft is a boutique software engineering studio. 35-50 senior engineers. Headquartered in Lisbon, Portugal. Engineering team in Ukraine.

We’ve been building production software since 2015. Our clients include Fortune 500 enterprises and funded startups across FinTech, LegalTech, EdTech, and AI. 5.0 Clutch rating across 30+ verified reviews. 200+ Upwork five-star reviews. Top Rated Plus and Expert-Vetted agency status (top 1%). Average client engagement: 3+ years.

Our AI engineering team works with Python, TypeScript, and the full LangChain/LlamaIndex/CrewAI ecosystem. Every AI engineer on our team has 5+ years of software engineering experience before they touched an LLM. The hardest part of production AI is not the model. It’s the system around it.

Lisbon HQ means EU jurisdiction, GDPR-native operations, and EU AI Act alignment. We’re in the same timezone as London and 5 hours ahead of New York, with enough overlap for daily standups and enough offset for focused deep work.

Ukraine engineering gives us access to one of Europe’s deepest talent pools: 300,000+ software developers, 23,000+ tech graduates annually, and an IT sector that exported over $6 billion in 2024. Our team has maintained uninterrupted delivery through distributed infrastructure, redundant power, and Starlink connectivity.

Industries

We build generative AI systems for clients in FinTech, LegalTech, EdTech, HealthTech, AI/ML, and eCommerce. Each vertical brings domain-specific compliance requirements (PCI DSS, HIPAA, GDPR, EU AI Act) that we’ve navigated before.

Case Studies

Byron / HiByron. Generative AI personal assistant platform. We built the conversation engine, context management, and multi-model routing for a funded AI startup. The system handles thousands of concurrent conversations with sub-second response times.

MyFlyRight. LegalTech passenger rights portal. Multi-year engineering partnership. The platform has recovered over €100M in compensation for EU passengers. We built and maintain the entire technology stack including document processing and automated airline communication.

HeyTutor. EdTech marketplace. Long-running partnership. We built the matching engine, payment system, and tutoring platform serving hundreds of thousands of students. The platform’s recommendation system uses ML-powered matching to connect students with optimal tutors.

Ready to talk? Contact us for a 30-minute technical discovery call. You’ll talk to a senior engineer about your use case, not a sales rep.

FAQ

Common questions

What are generative AI development services?
Generative AI development services cover the full lifecycle of building AI-powered software: strategy and use-case identification, data preparation, model selection (OpenAI, Anthropic, open-source), system architecture (RAG, agents, copilots), development, evaluation, deployment, and ongoing maintenance. The goal is production software that delivers measurable business outcomes, not a demo.
How much does a generative AI project cost?
A discovery sprint with a working prototype costs $25K-$60K over 4-8 weeks. An MVP build runs $80K-$250K over 3-5 months. A retained AI engineering team costs $40K-$90K per month depending on team size and seniority. EltexSoft's rates are $50-99/hr for senior engineers.
How long does a generative AI project take?
A proof of concept takes 4-8 weeks. An MVP with evaluation and production deployment takes 3-5 months. Complex multi-agent systems or full-product builds take 6-12 months. Most of our AI engagements are ongoing retained relationships, not one-off projects.
Why do most enterprise AI pilots fail?
MIT Project NANDA's 2025 study found 95% of enterprise AI pilots deliver no measurable P&L impact. The main causes: vague success metrics, poor data readiness, static systems that don't learn from feedback, and vendors delivering thin wrappers around GPT with no production engineering. The fix is specific: define success criteria before writing code, staff data engineering from day one, build evaluation loops, and commit to iteration.
What is RAG and when do I need it?
RAG (Retrieval-Augmented Generation) connects an LLM to your own data, including documents, databases, and knowledge bases, so it answers questions using your information rather than its training data. You need RAG when accuracy matters: customer support over your docs, internal knowledge search, compliance-sensitive answers, or any use case where hallucination is unacceptable.
Should we fine-tune a model or use RAG?
RAG is the right choice for factual recall over your own data. Fine-tuning is the right choice for changing model behavior, tone, or domain-specific reasoning patterns. Most projects start with RAG because it's faster, cheaper, and doesn't require training infrastructure. We help you decide based on your specific use case.
Which LLM should we use — OpenAI, Anthropic, or open-source?
It depends on your requirements. OpenAI GPT-5.5 excels at general reasoning and agentic coding. Anthropic Claude Opus 4.7 is strongest for long-context analysis and careful instruction-following. Open-source models (Llama 4, Mistral, Qwen) are best for on-premise deployment, data sovereignty, or cost optimization at scale. We're provider-agnostic and build abstraction layers so you can swap models without rewriting your application.
How do you handle data privacy and compliance?
Your data stays yours. We deploy in private VPCs, configure zero-data-retention with foundation model providers, and implement PII redaction in the pipeline. Our Lisbon headquarters means we operate under EU jurisdiction, with GDPR-native operations and EU AI Act alignment. We support SOC 2 and HIPAA compliance requirements.
What does your evaluation and testing process look like?
Every AI system we build ships with an evaluation harness. We use LLM-as-judge scoring, golden test datasets, faithfulness and groundedness metrics, and regression suites that run on every deployment. Observability is built in from day one using tools like Langfuse and Arize Phoenix, so you see exactly how the system performs in production.
Who owns the IP and the trained models?
You do. Full work-for-hire assignment. All code, prompts, fine-tuned model weights, evaluation datasets, and documentation are yours. This is standard in our contracts.
What happens if the underlying model is deprecated?
We build provider-agnostic abstraction layers. When OpenAI deprecates a model version or Anthropic ships a new Claude, we swap the model in the routing layer without rewriting your application. We've done this multiple times for existing clients.
What does post-launch support look like?
Most of our AI clients stay on a retained engagement after launch. We monitor model performance, evaluation scores, and cost. When accuracy drifts or a better model becomes available, we update. Our average client engagement is 3+ years, which tells you how ongoing support actually works here.

Tell us what you're building.

One business day reply. From an engineer, not a sales rep.

Talk to us