Custom AI Agents for Business Operations

• Operations teams where a single request touches email, CRM, documents, a calendar, and a spreadsheet before it is done.
• Sales and service organizations that want instant, accurate follow-up on every lead and ticket without adding headcount.
• Companies with internal experts who spend their day answering the same questions from colleagues and customers.
• Businesses that tried a chatbot and need something that can actually take action and be trusted to do so.

Agents that earn trust in stages

An AI agent is only useful if it can act, and only acceptable if it can be trusted to act. DPI resolves that tension with staged autonomy. The agent starts read-only, producing drafts and recommendations your team reviews. When its judgment matches yours on the evaluation set, it moves to suggest-and-approve. Autonomous action is granted per case type once the numbers justify it, and high-impact actions keep a human sign-off permanently.

Engineering that makes agents reliable

Reliability comes from the parts around the model: precise tool definitions with permission scopes, retrieval that grounds the agent in your records and procedures, memory that persists across steps in PostgreSQL, confidence thresholds that route uncertainty to people, and logs that let anyone replay a decision. We orchestrate with LangChain and LangGraph, which gives long-running workflows checkpoints, retries, and clean hand-offs between specialized agents.

Evaluation is built before the agent. We collect real requests, define correct outcomes with your team, and score every version of the agent against them. The same evaluation runs when a model is updated or your data changes, so drift is caught before customers notice.

Models and hosting

Agents can run on OpenAI, Anthropic Claude, AWS Bedrock, or self-hosted models such as Qwen on infrastructure you control. Mixed deployments are common: a strong hosted model for planning and reasoning, a small private model for routine extraction and classification. Data is minimized before prompts, and sensitive workloads stay inside your environment.

What an agent deployment returns

Tasks completed without human touch, response times for leads and requests, escalation rate and reasons, error rate versus the manual baseline, and cost per task. These are reported monthly alongside the evaluation results and the next tools or case types queued for the agent.