Problem
Staff and customers still dig through portals while chatbots guess—or invent—answers without your systems.
Solutions
Operational AI
Operational AI is AI inside live business processes: customer and employee assistants, RAG over approved knowledge, LLM selection and adaptation, document intelligence, recommendations, and the integrations that connect it all to CRM, helpdesk, BI, and APIs. We consult on the use case and controls, then develop systems that answer, act, and escalate with your data—not a chat demo that never leaves the sandbox.
Plan an AI assistant
AI assistants that answer, act, and escalate inside your workflow
Connect assistants to CRM, helpdesk, BI, and knowledge bases. Control access, allowed actions, and when a person must take over—for support, IT, HR, sales, and management. We design for grounded answers and measurable escalation, not open-ended chat that invents policy.
What we deliver
Assistants grounded in approved data that can act in workflows and escalate when confidence is low.
How it works
- Ground answers with retrieval from approved sources
- Show source references and apply role-based access
- Create tickets, update records, and trigger approvals
- Route sensitive or low-confidence cases to a human
Outcomes
- Faster first response with controlled knowledge
- Actions in CRM and helpdesk, not chat-only demos
- Measurable escalation and resolution rates
Assistants for specific operating roles
Customer support assistant
Answer common customer questions using approved product, service, policy, and order information. Create or update a support request when the issue requires a specialist.
Internal knowledge assistant
Give employees fast, controlled access to policies, procedures, technical documentation, project materials, and internal knowledge without searching across disconnected systems.
IT and service-desk assistant
Help employees resolve standard IT requests, retrieve instructions, capture issue details, categorise tickets, and route them to the correct support queue.
Sales and account assistant
Prepare account summaries, retrieve product information, support proposal drafting, and help sales teams respond faster with current and approved content.
Analytics and management assistant
Summarise business data, prepare management updates, identify key changes, and help teams investigate operational questions using approved datasets.
Designed for control, not uncontrolled conversations
Grounded answers
Use retrieval from approved sources rather than relying only on a model’s general knowledge.
Source references
Show where an answer comes from so users can verify important information.
Role-based access
Apply existing access rights so employees see only information they are permitted to use.
Human escalation
Route low-confidence, sensitive, or complex requests to an operator or specialist.
Workflow actions
Create tickets, update records, request approvals, send notifications, or trigger predefined processes.
Performance analytics
Track request volume, response time, resolution rate, escalation rate, unanswered questions, and user feedback.
Assess your knowledge base
Make enterprise knowledge usable without exposing uncontrolled data
RAG retrieves from approved sources before generating an answer. Relevant, traceable, and permission-aware—so you can update knowledge as documents change without retraining the model. We wire the corpus, access rules, and citations into the way your teams already search and work.
Problem
Enterprise knowledge lives in SharePoint, CRM, and wikis—models alone cannot answer safely from that sprawl.
What we deliver
RAG and search that retrieve approved content, respect permissions, cite sources, and refresh as documents change.
How it works
- Connect sources → index content → apply permissions → retrieve → answer with references
- Ingest SharePoint, DMS, CRM, helpdesk, portals, databases, and APIs
- Keep your data as the source of truth—update knowledge without retraining
- Monitor gaps and unanswered questions to improve the corpus
Outcomes
- Traceable answers employees can verify
- Permission-aware retrieval by role
- Knowledge that stays current without model rebuilds
Typical source systems
- SharePoint and internal file storage
- Document management platforms
- Product and technical documentation
- CRM and support-history records
- Helpdesk knowledge bases
- Corporate portals and intranets
- SQL and NoSQL databases
- Internal APIs and operational systems
Discuss LLM implementation
Adapt foundation models to your terminology and constraints
Prompt engineering, fine-tuning, guardrails, and evaluation against accuracy, latency, data sensitivity, and cost—so the model can run in production. Vendor and model agnostic: we pick and adapt GPT, Claude, Gemini, Llama, Mistral, or private models to your terminology and constraints.
Problem
A generic model misses your terminology, cost bar, and security rules—or burns budget on every request.
What we deliver
Model selection, adaptation, and evaluation so GPT, Claude, Gemini, Llama, Mistral, or private models fit the task.
How it works
- Choose models by accuracy, latency, data sensitivity, and cost
- Prompt engineering, fine-tuning, and structured evaluation
- Guardrails, content controls, and human review paths
- Vendor-agnostic routing so you are not locked to one API
Outcomes
- Models that speak your domain language
- Cost and quality under an explicit bar
- Production-ready controls, not a prompt experiment
Automate a document workflow
From unstructured files to structured decisions
OCR, extraction, classification, and report generation for operational and management teams—not summaries alone. Files become structured work items, routed into the processes that already own contracts, claims, invoices, and briefings.
Problem
Contracts, claims, and invoices pile up as PDFs while teams re-type fields and miss risk in the fine print.
What we deliver
Classification, extraction, review support, and reporting that turn files into structured work items.
How it works
- Classify document type, priority, and process route
- Extract fields, entities, dates, amounts, and obligations
- Summarise and compare contracts and policies under review controls
- Route incoming requests from messages and attachments into workflows
Outcomes
- Less manual re-keying into core systems
- Faster document cycles with audit-friendly outputs
- Management briefings from document volume, not guesswork
From unstructured files to structured work
Document classification
Automatically identify document type, topic, priority, business unit, or required process route.
Information extraction
Capture key fields, entities, dates, amounts, obligations, risks, and other specified information.
Contract and policy review
Summarise key provisions, compare documents, identify missing information, and support controlled review processes.
Management reporting
Consolidate large document volumes into structured briefings, risk summaries, status reports, and decision materials.
Incoming request processing
Read messages and attachments, identify intent, extract required data, and route each request to the appropriate team or workflow.
Build a recommendation engine
Recommend the next action from operational data
Ranking models with BI integration and A/B measurement so recommendations change support, sales, and operations—not just a homepage carousel. Next-best action and prioritisation sit on live behavioural and process data, with a clear baseline to prove lift.
Problem
Teams pick the next case or offer by habit while behavioural and process data already show a better path.
What we deliver
Ranking and next-best-action systems with feedback loops and BI so recommendations change real work.
How it works
- Next-best action for support, sales, and operations
- Product and content ranking with live feedback loops
- Operational prioritisation from behavioural and process data
- A/B testing and BI so recommendations can be measured
Outcomes
- Higher conversion or resolution on ranked actions
- Priorities driven by data, not queue order alone
- Measurable lift versus baseline behaviour
Map integrations
AI connected to the systems you already run
Reliable AI needs context and permissions. We integrate with CRM, helpdesk, BI, document repositories, APIs, and operational platforms under the access rules you already enforce—so the system can read, write, and log actions your audit function can review.
Problem
AI that cannot read or write your systems stays a chat window—context and permissions never leave the demo.
What we deliver
Integrations that put AI into CRM, helpdesk, BI, document stores, APIs, and databases under existing access rules.
How it works
- Map systems, identities, and the actions AI may take
- Connect CRM, helpdesk, ERP, BI, DMS, and internal APIs
- Enforce role and source permissions on every call
- Log actions and answers for audit and improvement
Outcomes
- AI that updates records and tickets, not only replies
- Reuse of your existing IAM and data rules
- Traceable automation across the stack
Plan the build with our engineers
Describe the process, data sources, and deployment constraints. We will advise on architecture, then develop the integrations and success metrics with you.
Talk to our AI engineers