01
AI customer support assistant
Answers routine questions from your own knowledge base then prepares replies for review and sends sensitive matters to a person.
Practical AI automation for real business workflows.
We turn repetitive daily work into reliable workflows. This includes sorting requests reading documents finding the right information and updating the systems your team already uses.
- 01Business inputForms, messages, files and events
- 02AI workflowClassify, retrieve, extract and draft
- 03Human reviewA clear approval gate where needed
- 04Business outputUpdate systems and deliver the result
Make routine work move.
Problems AI can help solve.
- Repetitive support questions
- Manual document review
- Slow reporting
- Scattered internal knowledge
- CRM data entry
- Lead qualification
- Manual data movement
- Repetitive admin tasks
Reduce manual work / Connect systems / Keep human oversight
We connect AI to your existing rules, tools, and approval steps. Repetitive, information-heavy work can move faster without becoming a black box.
We map the handoffs between people and systems, decide which steps are suitable for automation, and test the workflow on real examples before bringing it into daily operations.
If the same jobs return to the same people every week, there is usually a sensible way to automate the routine parts.
We begin with the operational bottleneck and keep people in control wherever judgment matters.
That gives the team a faster, more consistent process while judgment, accountability, and control stay in human hands.
What we actually build
These seven patterns cover the AI work most businesses need. Each connects to tools your team already uses.
- 02
RAG chatbot
Answers from selected business knowledge such as FAQs product documents manuals policies and SOPs instead of guessing from general model knowledge.
- 03
Document processing
Extracts structured fields from invoices contracts forms reports and résumés then routes exceptions to a person.
- 04
CRM automation
Scores leads updates records prepares follow ups summarises calls and moves deals through the pipeline without manual data entry.
- 05
Reporting automation
Collects data summarises changes updates the dashboard and highlights what needs attention.
- 06
Internal AI agents
Task focused assistants that follow defined rules use approved tools and stop for human sign off when it matters.
- 07
SaaS AI features
AI capabilities built into your existing product for search summarisation drafting and classification with admin controls and audit logs.
AI agents for defined work.
An AI agent is a workflow assistant that understands a task uses approved tools follows defined rules and stops for human oversight when needed.
- Lead qualification agent
- Internal knowledge assistant
- Reporting assistant
- Document review assistant
- CRM update assistant
- Customer support triage assistant
AI grounded in your knowledge.
RAG gives an assistant answers based on selected business knowledge instead of general model knowledge alone.
Grounded in
- Company FAQs
- Product documentation
- Training manuals
- Policies
- Client documents
- Internal SOPs
Turn document-heavy work into structured workflows.
Extract the information your team needs. Apply clear validation rules then move approved data into the systems where work continues.
InputExtractStructure
Controlled processingHuman review where confidence matters
- Invoices
- Contracts
- Forms
- Reports
- Résumés
- PDFs
- Scanned documents when OCR quality is good
Not every process should be automated.
We will tell you when AI is the wrong answer. These are categories we do not automate blindly even when it is technically possible.
- Legal decisions
- Medical decisions
- Financial approvals
- Sensitive HR decisions
- High-risk customer conversations
- Anything requiring final human judgment
- Anything using poor-quality or unverified data
AI should be useful controlled and responsible.
AI systems need clear limits human oversight data privacy permission controls review workflows and fallback options.
Barakode uses AI to improve operations while people retain responsibility for decisions.
- Defined limits
- Human oversight
- Data privacy
- Permission controls
- Review workflows
- Fallback options
From opportunity to production
Eight steps. The prototype uses your real inputs rather than a demo dataset.
01
AI opportunity discovery
Find where time goes and identify the work that can repeat safely.
02
Workflow mapping
Document the current process honestly including its exceptions.
03
Data and knowledge review
Review the source material and confirm that it is reliable enough to use.
04
AI architecture planning
Set the models retrieval tools permissions review gates and fallback steps.
05
Prototype
Test one complete workflow on real inputs.
06
Production build
Add integrations admin controls audit logs and error handling.
07
QA and safety checks
Test edge cases refusal behaviour and the response when the model is wrong.
08
Launch and improve
Compare the workflow with the manual baseline then improve it.
About AI automation.
All FAQsRarely. We identify the repeatable well defined parts then automate those while keeping human judgment where the cost of being wrong is high.
Yes. It can use your own content so answers come from your knowledge with a clear path to a person when needed.
Yes. Invoices contracts forms reports and résumés are common. We verify scanned document quality before committing to a workflow.
Yes when the CRM exposes an API. Lead scoring record updates call summaries drafted follow ups and pipeline movement are all standard.
Yes. Retrieval summarisation and classification can be added to the product you already run.
We set clear decision limits add human review for consequential work and use permission controls audit logs data privacy boundaries and a defined fallback.
