Enterprise knowledge AI
RAG systems that search policies, manuals, meeting notes, and FAQs and answer with references.
Custom AI Development
Custom AI built around your operations.
Solve workflows that off-the-shelf tools cannot address using generative AI, agents, RAG, MCP, and existing-system integration.
We support discovery, prototyping, product development, security design, launch, and continuous improvement even when requirements are not yet fully defined.
Definition
InnoSphere custom AI development
Custom AI development is not simply adding a model. We map users, workflows, decisions, data, existing systems, and approval ownership, then separate AI-assisted steps from human responsibilities and build a system that can operate in production.
Use cases
Systems we can build
Operational AI agents
Agents that collect information, classify work, draft content, call tools, and record outcomes.
Document and data processing
Extract information from PDFs, invoices, emails, and reports and route it through review and registration.
Customer operations AI
Inquiry triage, operator assistance, CRM integration, and multilingual customer workflows.
Forecasting and anomaly detection
Use operational data to support demand, inventory, equipment, and quality decisions.
AI inside existing SaaS
Add AI search, generation, recommendations, and execution to current web products and admin tools.
Delivery
Project process
01
Workflow and requirements
Clarify the problem, users, current process, data, exceptions, KPI, and initial scope.
02
Technical validation
Use a focused prototype to test quality, feasibility, cost, and risk before full development.
03
Production development
Integrate UI, backend, AI processing, APIs, permissions, logs, and testing.
04
Launch and improvement
Review adoption and KPI data and improve prompts, models, workflows, and user experience.
Scope
Scope and deliverables
Product and requirements design
Workflow maps, use cases, non-functional requirements, KPIs, and implementation roadmap.
AI and application development
LLMs, RAG, agents, web applications, backend services, and data processing.
System integration
Connections to existing environments through MCP, APIs, webhooks, and databases.
Evaluation and operations
Quality evaluation, access control, audit, cost monitoring, failure handling, and improvement procedures.
Governance
Designed to move beyond a prototype
A technically working demo is different from a system people can keep using, so operating conditions are designed from the beginning.
- Clear users and business ownership
- Model-output evaluation criteria
- Sensitive-data and retention policy
- Permissions, approvals, and audit logs
- API and model cost monitoring
- Maintenance, incident response, and improvement cycle
FAQ
Frequently asked questions
Can we talk before requirements are defined?
Yes. We start from the business problem and expected outcome and separate where AI is appropriate from where conventional software is better.
Can we begin with a small validation?
Yes. We can test one workflow with bounded data, confirm quality and economics, and then decide whether to proceed to production.
Do you support the system after launch?
Yes. Ongoing support can include model and API changes, quality evaluation, log analysis, and product improvement.
Work backward from the operation to the AI worth building.
Bring an idea or an unresolved problem. We will map the workflow, data, systems, and expected outcome and propose a focused validation and production path.