Put AI to work.
Start with a useful business task.
FiibixTech helps businesses plan and develop AI assistants, knowledge search and automated workflows. Connect the use case, your data and your existing systems before deciding what to build.
A question, document or operational request.
Relevant records, policies or connected systems.
Apply validation and human review where required.
Return an answer, prepare a draft or update an authorised workflow.
The implementation depends on the use case, permissions, available data and agreed review requirements.
Where could AI help your team?
These are example applications to explore during discovery. Feasibility and scope depend on your information, systems and business requirements.
AI Assistants & Chatbots
Help customers navigate approved information, prepare enquiries and find the right next step, with clear routes to your team.
Document & Knowledge Search
Make approved documents easier to search. Discuss source references, user permissions and how answers should be reviewed.
Workflow Automation
Connect repetitive tasks across existing systems, using defined rules and AI assistance where it serves a clear purpose.
Extraction & Classification
Explore extracting selected information from documents and organising it for review before it enters an operational system.
Enquiry & Sales Assistance
Help teams summarise enquiries, prepare drafts and organise CRM tasks while retaining approval over customer communication.
Reporting Assistance
Explore natural-language access to selected reports, summaries and business records, with validated calculations and appropriate access controls.
Define the task.
Agree on how to evaluate it.
A focused pilot helps you assess the proposed workflow before expanding it. Start with one task, representative examples and a clear acceptance process.
Choose the business task
Define the users, inputs, expected outputs and current process you want to improve.
Review the data and systems
Check source quality, permissions, available integrations and information-handling requirements.
Agree on evaluation criteria
Select representative examples and define how accuracy, usefulness, latency and operating cost will be assessed.
Test and decide the next step
Review results, exceptions and user feedback. Decide whether to refine, expand or change the approach.
Connect development with operational requirements.
Development areas to discuss
- Use-case discovery and workflow design
- Model and platform selection
- Knowledge retrieval and source integration
- Interfaces, APIs and system connections
- Evaluation and deployment planning
Operational areas to define
- Data access and user permissions
- Human review and approval points
- Fallbacks and exception handling
- Usage monitoring and operating budgets
- Ownership, documentation and support responsibilities
Your proposal will specify the actual deliverables. Model usage, hosting, third-party licences, data preparation and ongoing support costs are discussed during scoping.
Useful AI needs
the right connections.
Share the tools your business uses. We’ll discuss API availability, access requirements and which workflow steps can be connected.
Integration feasibility depends on the platform, available APIs, permissions and project requirements.
Start with
clear expectations.
Based in Ahmedabad, India, we welcome AI development and automation enquiries from local and international businesses.
What is the difference between AI and rule-based automation?
Rule-based automation follows defined conditions and actions. AI can help interpret less structured inputs, such as questions or documents. A project may combine both approaches.
Can an assistant use our own documents?
We can explore connecting approved documents to a retrieval-based assistant. Source quality, permissions, references and answer evaluation need to be included in the design.
Will the AI always give a correct answer?
No. AI outputs can contain mistakes. The project should define evaluation, source checks, fallback behaviour and human review appropriate to the intended task.
How is our business data handled?
Data handling depends on the selected architecture and providers. Before implementation, discuss what information is processed, where it is stored, who can access it and the applicable provider terms.
How much does an AI project cost?
Cost depends on the workflow, data preparation, integrations, evaluation and deployment requirements. Ongoing model usage, hosting and support costs should also be included in planning.
Can we begin with one workflow?
Yes. Discuss a focused pilot with agreed inputs, outputs and evaluation criteria before expanding the solution to additional tasks.
Which task would you
like your team to handle better?
Tell us about the workflow, your current tools, available information and the outcome you need. We’ll discuss a practical starting point.