Generative AI Consulting Services

Our Generative AI Development Services turn an approved use case into a production system. We build the application, connect it to existing tools and data, test it against agreed requirements, and prepare it for rollout.

RAG development connects a model to approved company knowledge with retrieval, source, and access controls. We design and build the ingestion and retrieval pipeline around source volume, update cadence, permissions, and response-time requirements.

Your current workflows and systems shape a practical, multi-year adoption roadmap.

Before development starts, the team agrees on the KPIs and the exact points where the project moves forward or stops.

Your budget stops funding disconnected experiments.

You get a Board-ready strategy tied to your P&L, so the budget goes to initiatives with a credible business case.

An audit of your existing workflows identifies the friction points where GenAI can deliver the fastest return on investment.

Each scenario is then compared against agreed criteria for technical feasibility, data readiness, expected business value, and implementation risk.

You invest only in use cases with a credible business case.

Operations and finance often reveal the clearest early returns. We put those practical wins first, then prepare for longer-term bets.

Data testing shows whether the sources can support the use case before model selection begins. RAG development then connects approved company knowledge to the system with source controls and access rules.

The Retrieval-Augmented Generation (RAG) pipeline fits the environment you already run. The database and retrieval components follow the source volume, update cadence, access rules, and response-time requirements.

Answers can draw from approved company sources instead of relying only on the model’s internal knowledge. This makes the source of an answer easier to check and can reduce unsupported responses without requiring a custom foundation model.

Generative AI (GenAI) software consulting services compare proprietary and open-weight models against your latency and cost requirements.

The resulting deployment architecture can range from scalable public cloud to private on-premises or air-gapped environments.

You get a model and deployment setup that fit the workflow without paying for capacity or controls the use case does not need.

We evaluate models against agreed quality, cost, latency, and security requirements.

Compliance guardrails can include automated audit logging, role-based access controls, and PII redaction pipelines.

Technical controls are mapped to the security, privacy, and governance requirements identified with your legal, compliance, and cybersecurity teams. The client’s responsible teams determine which requirements apply to the use case and jurisdiction.

Where audit logging and access controls are included in the agreed scope, legal and cybersecurity teams can review how the system is used and who can access it.

The 10-20-70 principle guides how GroupBWT allocates transformation effort. Most of that effort goes into the people, processes, and cultural changes that determine whether teams use the system.

The affected workflow is rebuilt from end to end. Each role then gets training based on the tasks it will perform with AI.

The system creates value only when people use it.

Clear roles and hands-on practice help your workforce use AI in daily work, where adoption can translate into sustained EBIT growth.