AI solutions
Practical AI engineering for the systems you already run.
We integrate AI into existing business applications: agents, knowledge assistants, document processing and WhatsApp automation, connected to your data and working within your permissions, business rules and approval steps.
Our approach
AI that works inside your business, not around it.
Most organisations do not need a separate AI product. They need AI in the places where work already happens: the order system, the support desk, the document workflow, the reporting dashboard.
We treat AI as one component of a production application. It reads from approved data, acts through defined APIs, and stays within the same authorisation, validation and audit rules as the rest of the software. Where a decision matters, a person reviews it.
What stays in place
- Your existing business rules and data validation
- User permissions and access control
- Human approval where decisions carry risk
- Logging of AI requests and responses
AI capabilities
Thirteen ways we put AI to work.
Each capability is engineered into your application and scoped to a clear business task.
AI Agent Development
Assistants that follow a defined task, call approved tools and APIs, and hand back to a person when a step needs judgement or approval.
Multi-Agent Systems
Several specialised agents that each handle one part of a process, such as research, drafting and checking, coordinated by explicit rules.
RAG Solutions
Retrieval-augmented generation that grounds answers in your own documents and data, with sources the user can check.
Enterprise Knowledge Assistants
Internal assistants that answer questions from policies, manuals and records, limited to what each user is allowed to see.
LLM Integration and Orchestration
Language models integrated into existing applications, with prompt management, output checks and model choice per task.
AI Chatbots and Conversational Apps
Conversational interfaces for customers or staff, connected to real business data and with a clear route to a human.
WhatsApp AI Automation
WhatsApp conversations connected to your systems for enquiries, updates and support, with AI-assisted replies where configured.
Document Intelligence and Data Extraction
Reading invoices, forms and contracts to extract structured data, with validation and review before it enters your systems.
Intelligent Search
Search that understands meaning as well as keywords, across documents, products or records, while respecting access rules.
AI-powered Workflow Automation
Routine steps such as classifying requests, drafting replies or routing work, handled by AI within an existing workflow.
AI-powered Reporting and Insights
Plain-language summaries and questions over business data, built on the same figures your reports already use.
Human-in-the-Loop AI Workflows
Review, approve and correct steps designed into AI workflows, so people stay responsible for the decisions that matter.
AI API and Gateway Integration
A central gateway or API layer for AI requests, so applications share one contract, one set of controls and one view of usage.
Business use cases
Where AI helps in day-to-day operations.
Staff spend time searching manuals, policies and past records for answers
A knowledge assistant answers from approved documents and shows where each answer came from.
Invoices, forms and documents are re-keyed into business systems
Document extraction captures the data, validates it and queues uncertain fields for review.
Customers ask the same product and order questions on WhatsApp every day
WhatsApp automation answers from your store data and hands complex conversations to your team.
Requests arrive by e-mail and need to be sorted, routed and acknowledged
AI classifies each request, drafts a first reply and routes it into the right workflow for approval.
Several applications call AI providers directly, each in its own way
An AI gateway standardises requests, applies shared controls and tracks usage in one place.
Managers need quick answers from reports without waiting for analysis
AI-assisted reporting summarises the figures your dashboards already hold, in plain language.
Architecture
How we build AI-enabled business applications.
AI sits alongside the application’s business logic, permissions and data validation. It does not bypass them. Each layer has one job, so AI can be added, changed or switched to another provider without rewriting the application.
User interface
Where people interact with the system.
- Web
- Mobile
- Admin applications
Connected by: Authenticated requests
Application layer
Existing workflows, authorisation and business rules.
- Business workflows
- Authorisation
- Business rules
- Validation
Connected by: Task-based AI requests
AI gateway
Centralised AI request handling and provider routing.
- API contracts
- Provider routing
- Usage tracking
- Retries and timeouts
Connected by: Provider calls
AI services
Configured models and retrieval services.
- LLMs
- RAG
- Embeddings
- Other configured services
Connected by: Approved data access
Data layer
Approved sources of business data and knowledge.
- Databases
- Documents
- APIs
- Knowledge sources
Connected by: Jobs and events
Workflow layer
Work that runs in the background or needs approval.
- Background jobs
- Approvals
- Retries
- Long-running processes
RAG and knowledge assistants
Answers grounded in your own knowledge.
Retrieval-augmented generation finds the most relevant passages in your approved sources and gives them to the model as context, so answers reflect your information rather than general knowledge.
Approved knowledge sources
Documents, policies, records and databases you choose to include.
Processing and chunking
Content is cleaned, split into passages and tagged with source and access rules.
Embeddings and retrieval
Passages are indexed so the most relevant ones can be found for each question.
Context-aware AI response
The model answers from the retrieved passages and cites its sources.
Reference implementations and products
Four kinds of AI and automation work.
Infrastructure, an integrated application, a product and an integration offering. Each is labelled with what it is, so they are never mistaken for one another.
Sugam AI OS
AI gatewayA centralised AI gateway that standardises how business applications send AI requests to configured providers.Sharpflux role: development and implementation.Bandhu
AI-integrated applicationAn application integrated with the Sugam AI gateway, moving from direct provider calls to standardised, task-based gateway requests.Consumes the Sugam AI gateway.Product WhatsApp Robot
WhatsApp business automationA WhatsApp-based product interaction and business automation solution that makes product information and customer interactions more accessible through conversation.WhatsApp for E-commerce
E-commerce integrationAn integration solution that connects WhatsApp to an existing online store, for product enquiries, order conversations and customer support.Scoped and integrated per store.
Reference implementations show engineering work and Sharpflux’s role in it. They are not Sharpflux-owned products.
Reference implementation
Sugam AI OS: one intelligent gateway for your AI applications.
Classification: AI Gateway / Reference Implementation
Sugam AI OS provides a centralised gateway for integrating AI capabilities into business applications. It helps standardise AI communication, manage provider routing, monitor usage and costs, and apply consistent validation and security controls.
Instead of every application calling AI providers directly, applications send requests to Sugam through standardised API contracts. The gateway routes each request to a configured provider, such as Gemini or ChatGPT, and returns a consistent response.
Its architecture follows a Bifrost-based thin LLM gateway approach: the gateway owns AI communication and its controls, while business logic stays in the applications that use it.
- What it is
- Centralised AI gateway layer
- Sharpflux role
- Development and implementation
- R&D and architecture
- Completed by the client before Sharpflux’s role began
- Gateway approach
- Bifrost-based thin LLM gateway
Business applications
- Bandhu
- Other business applications
Sugam AI Gateway
- Standardised API contracts
- Provider routing
- Request validation
- Security controls
- Logging and observability
- Token and cost tracking
- Retry and timeout handling
Configured AI providers
- Gemini
- ChatGPT
- Other configured providers
Responses return to the calling application through the same standardised contract.
Scope and accuracy
- The client completed Sugam’s research, architecture, requirements and documentation before Sharpflux became involved. Sharpflux’s role is development and implementation.
- Responsibilities shown describe the gateway architecture. The production status of individual capabilities is confirmed per environment and is not claimed here.
- Sugam is a centralised AI gateway layer. It is not presented as an autonomous agent platform.
- Provider names identify services the gateway can be configured to use. No partnership or endorsement is implied.
Reference implementation
Bandhu: bringing centralised AI capabilities into business applications.
Classification: AI-integrated Application / Reference Implementation
Bandhu is an application integrated with the Sugam AI gateway.
Its integration approach moves AI communication away from direct calls to individual AI providers and towards standardised, task-based requests sent through Sugam. Bandhu describes the task it needs done; the gateway handles provider routing and returns the response.
Sugam and Bandhu are distinct: Sugam is the centralised gateway layer, and Bandhu is an application that consumes it.
- What it is
- Application consuming the Sugam AI gateway
- Relationship to Sugam
- Gateway consumer, not part of the gateway
- Integration style
- Task-based requests through the gateway
- Engineering focus
- Application-to-gateway integration
Bandhu application
Identifies the AI task it needs.
Task-based AI request
Sent through the gateway’s standard contract.
Sugam gateway
Validates the request and routes it.
Configured AI provider
Processes the request.
Response to Bandhu
Returned in a consistent format.
Scope and accuracy
- Only Bandhu’s integration relationship with Sugam is described here. Its business domain, users and end-user features are not published.
- Further engineering detail will be published once confirmed with the project team.
WhatsApp for E-commerce
Bring your store closer to your customers.
Extend your e-commerce experience into WhatsApp. Let customers discover products, ask questions, share attachments, receive order-related updates and communicate with your business through a familiar messaging experience.
WhatsApp for E-commerce is an integration solution. We connect WhatsApp to your existing store or backend through approved APIs, so product and order conversations use your real data, with handover to your team when a person is needed.
Each integration is scoped to your platform and to WhatsApp Business Platform policies. Workflows are agreed per project; none is assumed to be live by default.
Typical conversations
- Product enquiries and catalogue sharing
- Order enquiries and status updates
- Support and return-related questions
- Handover to a member of staff
FAQs
Questions about AI solutions.
Do we need to replace our existing software to add AI?
No. In most projects we add AI to the applications you already use, through APIs and a defined integration layer. Your existing workflows, data and business rules stay in place.
How do you stop AI from bypassing our permissions and business rules?
AI requests go through the application layer, which applies the same authorisation and validation as any other request. Retrieval is limited to data the user may see, and actions are restricted to approved APIs, with human approval where needed.
What is an AI gateway, and when is it worth having one?
An AI gateway is a central layer that every application uses to send AI requests. It standardises the request format, routes to configured providers and applies shared controls such as validation, logging, usage tracking, retries and timeouts. It becomes worthwhile once more than one application, or more than one provider, is involved.
When should we use RAG instead of a general-purpose chatbot?
Use RAG when answers must come from your own documents or data, such as policies, product information or records. Retrieval gives the model the relevant passages as context and lets users check the sources behind each answer.
Will AI replace our team?
That is not how we design AI systems. We use AI to handle routine steps and to help people find information faster, while decisions that carry risk stay with your staff through review and approval steps.
Which AI models and providers do you work with?
We select models per task and integrate providers such as OpenAI, Anthropic Claude, Google Gemini, Amazon Bedrock, Meta Llama and Azure AI. Provider names indicate technologies we integrate with, not partnerships.