Agentic RAG & AI Application Development

From RAG to agentic RAG

Traditional RAG retrieves relevant information and generates a response. It works well for single questions but struggles with complex, multi-step requests. Agentic RAG adds an agent layer that plans the steps, chooses the right tools and data sources, checks its own intermediate results and can trigger real actions, such as creating a ticket or updating a record.

What we build

• Knowledge assistants that answer from your policies, manuals and internal documents, with sources.
• Agentic workflows that look up data, check eligibility or rules, and complete routine tasks end to end.
• AI features inside your existing products, such as search, summaries, drafting and classification.
• Integrations with managed AI platforms, including Progress Agentic RAG and the major cloud providers' AI services.

Where agentic RAG pays off

• HR and benefits: retrieve policies, check eligibility and automate routine updates.
• Healthcare: find clinical data, interpret results and draft reports for review.
• Finance and compliance: analyze regulatory data and generate compliance summaries.
• Customer support: fetch answers, trigger requests and log issues without manual hand-offs.

How an AI engagement runs

• Discovery: we agree the use case, the data sources and how success will be measured.
• Prototype on your data: a working assistant or agent built on a representative slice of your real data, not a demo dataset.
• Evaluate: we test answer quality and task success against the agreed metrics before anyone relies on it.
• Integrate and roll out: we connect it to your systems and release it gradually to real users.
• Monitor and improve: we track quality in production and tune retrieval, prompts and models as they evolve.

Connecting AI to your data

Much enterprise knowledge lives in relational databases, not documents. We connect AI systems to Progress OpenEdge, SQL Server, PostgreSQL, Oracle and MySQL through APIs and JDBC/ODBC connectors, normalize the data into a vector store, and orchestrate retrieval and reasoning with frameworks such as LangChain. Each layer can be swapped as models and tools improve.

AI-driven reporting and analytics

Not every AI project needs an agent. Often the fastest win is turning the data you already have into insights people act on. We build AI-powered reporting and analytics that support faster, smarter decisions, from dashboards to predictive models.

• Dashboards and self-service reporting in Power BI, Tableau and ThoughtSpot.
• Predictive and machine-learning models with scikit-learn, TensorFlow and PyTorch.
• Natural-language questions over your reports and data, using retrieval-augmented generation.

Built to be trusted

We define how quality will be measured before we build, and we monitor it after launch. Guardrails, human sign-off where it matters, and clear logging keep the system's behaviour explainable to your users and auditors.

Your data stays under your control. We design access so the AI only sees what each user is allowed to see, and we choose hosting and model providers that fit your security and compliance requirements.

Engagement models

Pick the model that fits your budget, timeline and how much you want to manage day to day. You can start with one and move to another as the work changes.

• Staff augmentation: individual engineers join your team, work in your tools and ceremonies, and report to your leads.
• Dedicated team: a stable squad that we build around your roadmap, with a Five Frogs lead responsible for delivery and quality.
• Fixed-scope project: an agreed scope, timeline and price for well-defined work such as a module, migration or MVP.