Custom AI Agents
Agents scoped to defined tasks and connected to business context, APIs, application data, and controlled workflow actions.
AI Agent Development Services
AI Agents and Automation Built Around Real Business Workflows
Digital Sensei designs AI agents and automation around the way your business actually works—connecting models with software, APIs, data, roles, and operational systems instead of treating AI as a standalone chatbot.
Business-first WorkflowsModels + APIs + DataHuman Oversight
Not every workflow needs AI.
AI is useful when a workflow needs to interpret language, work with unstructured context, support a conversation, or assist a judgment. It should augment dependable application logic—not replace it blindly.
We begin with the business objective, map the workflow and decision points, then choose AI, deterministic automation, or a deliberate combination of both.
Useful AI intervention points
- Understand natural-language input and intent
- Extract structured information from unstructured content
- Summarize context and draft grounded responses
- Support qualification, routing, coaching, and guided interaction
- Turn approved business context into a recommended next step
- Hand controlled actions back to the application workflow
AI agents and automation connected to the systems where work happens.
The right solution may be an agentic workflow, an AI-assisted step inside existing software, or straightforward automation with no model involved.
AI Workflow Automation
AI-assisted classification, routing, summarization, content handling, and workflow progression with deterministic controls where needed.
Conversational AI
Text-based, context-aware interactions for guided assistance, qualification, information gathering, and service workflows.
AI-Enabled SaaS Features
Coaching, analysis, assistance, and workflow features inside dashboards, operational tools, and established SaaS products.
Knowledge & Context Integrations
Controlled use of approved business information, application data, structured instructions, and workflow context.
AI + API Orchestration
Models connected through backend APIs to notifications, third-party services, databases, and existing business platforms.
AI-enabled products—and the deterministic automation foundation behind reliable workflows.
Synqro.ai and HeyLibby demonstrate AI connected to real software and services. The remaining projects are explicitly non-AI examples of the business rules, state changes, notifications, and scheduled processes that effective automation still depends on.
AI-enabled system
Synqro.ai
A React, NestJS, and PostgreSQL sales training and enablement SaaS that brings together AI conversation practice, coaching workflows, readiness and performance visibility, and reporting. Conversational and voice workflows integrate with Retell AI while backend APIs and structured product logic manage the wider experience.
AI SaaS • Conversational Workflows • Coaching • APIs • Reporting
AI-enabled system
HeyLibby
An AI voice agent and virtual receptionist connecting phone conversations to service answers, requirements gathering, lead qualification, booking, multilingual workflows, and human handoff. It orchestrates Twilio Media Streams, OpenAI, Deepgram STT, and ElevenLabs TTS around the business process.
OpenAI • Workflow Automation • Qualification • Booking • Integrations
Anleggsauksjon · Norway
Auction Portal
Anleggsauksjon is an operational equipment auction platform serving the Norwegian market. Its configured forms, bidding and auto-bidding rules, outbid notifications, highest-bidder handling, and commission processes demonstrate deterministic workflow automation rather than AI.
Workflow Automation • Business Rules • Notifications • Marketplace Logic
Deterministic automation — not AI
Delimed
A Paris MERN web and mobile medicine-ordering platform coordinating customer, pharmacy, and administrative roles. Approval flows, pharmacy pricing, Stripe capture after customer approval, and pickup or delivery are governed by application rules and order states.
MERN • React Native • Stripe • Multi-role Workflow
Automation without AI
EaziDriver
A Glasgow taxi-driver React Native application that aggregates event and road-closure data, refreshes it through scheduled processes, and supports favorites and notification workflows. It shows how useful automation can be driven entirely by data and business rules.
Data Aggregation • Notifications • Scheduled Updates • Business Rules
Connect context, models, application logic, and controlled actions.
The exact architecture depends on the product. A practical AI agent commonly moves through layers like these, with the application retaining control over permissions and critical rules.
User or system input
Text, application events, business data, or external requests start a defined workflow.
Business context
Product data, user roles, approved information, workflow state, and structured instructions shape the task.
AI or model layer
OpenAI and ChatGPT, Claude, Gemini, or another suitable model interface handles the bounded language or reasoning step.
Application orchestration
Node.js, NestJS, REST APIs, validation, and workflow logic determine what the software may do next.
Connected systems
PostgreSQL, MongoDB, notifications, payments, telephony, business platforms, and third-party APIs provide or receive data.
Controlled output or action
The result may be a response, classification, workflow update, notification, handoff, or recommended next step.
AI where judgment helps. Software rules where certainty matters.
Reliable business automation often combines both. The model handles ambiguity; the application enforces the rules that should not be ambiguous.
Language, context, and assistance
- Intent understanding and classification
- Summarization and contextual responses
- Drafting and conversational interaction
- Extracting structured meaning from unstructured input
Control, integrity, and certainty
- Authentication, permissions, and approvals
- Payment capture and calculations
- Validation and database constraints
- Critical state transitions and business rules
Choose the provider around the workflow—not the trend.
Digital Sensei works with OpenAI and ChatGPT, Claude, and Gemini integrations. Selection depends on required capabilities, latency, context, integration constraints, cost, and the wider product requirements; no single model is universally best.
Integration capability
- Models: OpenAI / ChatGPT, Claude, and Gemini
- Conversational services: Retell AI, Twilio, Deepgram, and ElevenLabs
- Backend: Node.js, NestJS, and REST APIs
- Data: PostgreSQL and MongoDB
Already have a product? Add AI where it creates value.
An existing SaaS product, React or Next.js application, React Native app, Node.js or NestJS backend, admin portal, or workflow system can gain a focused AI capability without turning the whole product into an AI experiment.
Understand the current workflow → identify a useful AI intervention → integrate incrementally → measure behavior → evolve.
Practical additions
- Conversational or context-aware assistance
- Classification, extraction, and summarization
- Coaching and guided workflows
- Lead or request qualification
- External model and API integration
- AI-assisted steps inside existing automation
Engineer AI behavior as part of the product.
An AI integration should not operate as an uncontrolled black box. Its scope, inputs, outputs, permissions, failure paths, and handoff points should be designed alongside the rest of the workflow.
Bound the task
Give the model a defined job, appropriate context, and explicit limits instead of open-ended authority.
Control critical actions
Keep permissions and important business rules in application logic, with input and output checks where appropriate.
Design human handoff
Include review, approval, escalation, or handoff when a person should remain part of the decision.
Test realistic scenarios
Evaluate expected journeys, edge cases, failure paths, and how model behavior affects the complete application workflow.
Observe important workflows
Use suitable logs and operational visibility to investigate behavior and improve the implementation after launch.
Evolve deliberately
Refine context, prompts, rules, and integrations as the product, provider landscape, and business process change.
From business problem to integrated, testable workflow.
Each stage keeps the AI behavior connected to real users, product rules, supporting systems, and measurable operating needs.
Discover
Understand the problem and determine whether AI, deterministic automation, or both are appropriate.
Design the workflow
Define inputs, context, decisions, actions, human handoff, and expected outcomes.
Architect
Choose models, APIs, data boundaries, backend integration, and control mechanisms.
Build & integrate
Connect models to product workflows, APIs, data, and supporting services.
Test & QA
Test realistic scenarios, failure paths, workflow behavior, and application rules.
Deploy & evolve
Release, observe behavior, and refine context, prompts, rules, and workflows.
Post-launch evolution for changing workflows and providers.
Support can be fixed-term, ongoing, or flexible-resource based, depending on the product and operating needs.
Refine behavior
Improve prompts, approved context, deterministic rules, handoffs, and workflow paths based on observed use.
Extend the system
Add integrations, product features, automation paths, backend changes, or appropriate provider updates.
Support production
Troubleshoot important workflows, maintain connected software, and help the implementation evolve with the product.
Business-first AI backed by full-stack product engineering.
AI agent development works best when model behavior, traditional software, integration boundaries, and the real operating process are considered together.
Business-first automation
We understand the objective and workflow before choosing where AI or conventional automation should intervene.
AI + software engineering
Real AI-enabled product experience is supported by MERN, Node.js, NestJS, API, database, SaaS, web, and mobile capability.
Connected delivery
Senior technical involvement can span workflow design, architecture, implementation, QA, deployment, and post-launch evolution for international product teams.
Digital agencies and technology partners can also engage Digital Sensei when additional AI integration, automation or product-engineering capability is required.
Practical questions about AI agents, integrations, and workflow automation.
What types of AI agents can Digital Sensei build?
We build agents for defined business tasks such as contextual assistance, qualification, information gathering, classification, summarization, coaching, routing, and controlled workflow actions.
What is the difference between an AI agent and normal automation?
An AI agent can interpret language or context and help choose a response or next step. Normal automation follows explicit rules. Many dependable workflows combine the two.
Does every business workflow need AI?
No. Permissions, calculations, payments, approvals, and known state transitions are often better handled by deterministic software. We recommend AI only where it adds useful capability.
Can you integrate OpenAI or ChatGPT into an existing application?
Yes. We can connect OpenAI or ChatGPT to an existing product through its backend, APIs, application data, user roles, and defined workflow controls.
Do you work with Claude and Gemini?
Yes. Digital Sensei supports Claude and Gemini integrations as well as OpenAI. Provider selection follows the task, context, latency, cost, and integration requirements.
Can an AI agent connect to APIs and databases?
Yes. A backend can provide approved context from PostgreSQL or MongoDB and expose controlled API actions while preserving permissions, validation, and application rules.
Can you add AI to an existing SaaS product?
Yes. We can identify a bounded intervention—such as assistance, summarization, coaching, or classification—and integrate it incrementally into the existing product.
Can AI agents trigger actions in business systems?
Yes, where appropriate. Actions should pass through controlled application logic and APIs so permissions, validation, and critical rules remain enforceable.
How do you decide what AI should handle?
We map the objective, input, context, decisions, risks, actions, and handoffs. AI suits ambiguous language or contextual assistance; conventional code suits rules requiring certainty.
Can AI workflows include human approval or handoff?
Yes. Human review, approval, escalation, and handoff can be designed as explicit parts of the workflow rather than treated as exceptions.
Can you build conversational AI?
Yes. We build text-based conversational workflows and have verified experience integrating voice conversation services. This page focuses on the wider business workflow and system orchestration.
Can you integrate AI with a Node.js or NestJS backend?
Yes. Node.js and NestJS can orchestrate model calls, data access, REST APIs, validation, business rules, and actions across connected systems.
Do you support AI systems after launch?
Yes. Support can cover troubleshooting, workflow and context refinement, new integrations, provider updates, backend changes, and continued feature development.
Can you modernize an existing automation workflow using AI?
Yes, after reviewing the current process. We can retain dependable rules and add focused AI assistance where language, context, or judgment creates a genuine benefit.
How does an AI automation project usually start?
It starts with the business process: the goal, current workflow, available data, decision points, actions, constraints, and where people need to remain involved.
Have a workflow that AI or automation could improve?
Share the process, software product, or operational workflow you want to improve. Digital Sensei can help determine where AI adds value, where deterministic automation is better, and how the complete solution should be integrated.