AI agents that run your business, not demos
We build autonomous agents that process documents, automate decisions and integrate your systems. Production-grade, audited, with measurable ROI from month one.
We implement AI where it creates real value: process automation, knowledge extraction from documents, and agents that understand your business. Before writing a single line of code, we audit your data quality, define success metrics and establish governance. Every project includes hallucination monitoring and inference cost control.
We work exclusively on AWS Bedrock and Anthropic Claude — the only infrastructure stack we trust for enterprise production workloads.
What is an enterprise AI agent?
An enterprise AI agent is a software system that combines a language model (LLM) with tools, memory and business logic to autonomously complete multi-step tasks. Unlike a simple chatbot, an agent can query your databases, call external APIs, write and execute code, and make decisions — all without human intervention at each step. In Adoredev we build three types: RAG systems for internal knowledge retrieval, autonomous agents for complex workflows, and conversational agents for customer-facing automation.
Our capabilities
AI solutions with measurable production results
AI Agents
AutonomousMulti-step agents with tool-calling, persistent memory and integration with your existing systems and databases.
Learn moreAWS Bedrock
Foundation ModelsRAG architectures, fine-tuning and serverless inference on AWS. Claude, Llama, Titan — the right model for each use case.
Learn moreConversational Agents
WhatsApp · Slack · WebChatbots and voice assistants with memory, context and personality deployed on every channel your customers use.
Learn moreLaravel & Python
BackendRobust backend infrastructure for AI systems: APIs, data pipelines and integrations with legacy systems.
Learn moreAnthropic Claude Suite
Claude APIOfficial integrations with Anthropic's API. Claude 3.5 Sonnet, Opus — with prompt engineering and context optimization.
Learn moreNVIDIA AI Stack
GPU InferenceNIM microservices for optimized LLM, vision and speech inference on GPU infrastructure — on-premise, cloud or edge.
Learn morePoC vs Production
Most AI projects die at the proof-of-concept stage. Beautiful demos that work with 10 documents but collapse with 10,000. Models that hallucinate critical data. Inference costs that explode at scale. We deploy in production: RAG systems with audited precision, robust data pipelines, and hallucination monitoring. If it is not in production processing real data, it is not finished.
Good fit
- Companies with their own data (documents, history, internal knowledge bases)
- Operations with repetitive tasks consuming 20+ hours per week
- Teams that have tried ChatGPT and need something production-ready
- Organizations with compliance requirements (EU AI Act, GDPR, HIPAA)
Not a good fit
- No structured data or internal documentation
- Projects 'to see what AI does' without a defined use case
- Expectation of results without investment in data quality
- Startups without established processes to automate
Our methodology
From data audit to production in 12 weeks
Data Audit
Assessment of your data quality and volume. We identify which use cases are viable with your current information and which require enrichment.
Bounded Proof of Concept
PoC with real data and defined success metrics. Maximum 4 weeks to validate technical viability before investing in production.
Production Development
Scalable architecture with hallucination monitoring, inference logging and cost controls. Testing with identified edge cases.
Deployment & Governance
System in production with performance metrics, configured alerts and EU AI Act compliance documentation if applicable.
Frequently asked questions about AI agents
Answers to the most common questions about enterprise AI implementation
We build three main types: RAG agents (Retrieval-Augmented Generation) that answer questions about internal documentation, autonomous agents for multi-step workflows such as document processing or decision automation, and conversational agents for customer interactions on WhatsApp, Slack or the web.
The more structured your data, the faster we can deliver. But we have worked with everything from well-organized Confluence wikis to scattered PDF archives. Our first step is always a data audit to determine viability before committing to a timeline.
We implement three layers: retrieval with semantic relevance scoring so the model only answers based on your real documents, output validation with automated test suites, and production monitoring with alerts when confidence drops below threshold. We never deploy without a hallucination audit.
It depends on volume and model. A typical internal knowledge agent for 50 employees using Claude Haiku costs between $50–$200/month in inference. We include cost projections in every proposal and build cost controls into every architecture by default.
Yes. We assess risk classification, implement the required technical documentation, and design the governance controls mandated by the Act for high-risk systems. If your use case touches HR, credit scoring or sensitive personal data, we flag this at the audit stage.
AI Viability Audit.
We evaluate your data and tell you what is possible. AI, security and production performance.
We evaluate your data quality and present viable use cases with estimated ROI.
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