01
AI customer support assistant
An AI customer support assistant answers routine questions from your knowledge base, drafts replies for review, and routes sensitive cases to a person.
AI workflow automation forsupport, documents and data.
We automate repeatable work across customer support, document processing, internal knowledge, CRM, and reporting. Each AI workflow connects to your existing tools and includes human review where judgment matters.
- 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
Turn repetitive tasks into AI workflows.
Business bottlenecks AI can address.
- 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
Seven practical use cases for AI customer support, document processing, CRM, reporting, and knowledge search, connected to your existing tools.
- 02
RAG chatbot
A RAG chatbot retrieves answers from approved FAQs, product documentation, policies, and SOPs, with a clear route to human support when a source is missing.
- 03
Document processing
AI document processing extracts structured fields from invoices, contracts, forms, reports, and résumés, then sends unclear cases for human review.
- 04
CRM automation
CRM automation helps qualify leads, update records, draft follow-ups, summarize calls, and keep the sales pipeline current.
- 05
Reporting automation
Reporting automation gathers data, summarizes changes, updates dashboards, and flags exceptions that need attention.
- 06
Internal AI agents
Internal AI agents handle defined tasks with approved tools, permissions, and a human approval step for consequential actions.
- 07
SaaS AI features
Add AI search, summarization, drafting, and classification to an existing SaaS product with admin controls and audit logs.
AI agents for defined work.
An AI agent follows a defined task, uses approved tools, and pauses for human review when the decision or data requires it.
- Lead qualification agent
- Internal knowledge assistant
- Reporting assistant
- Document review assistant
- CRM update assistant
- Customer support triage assistant
AI grounded in your knowledge.
A retrieval-augmented generation (RAG) chatbot searches approved business documents before drafting an answer, so responses can be checked against your sources.
Grounded in
- Company FAQs
- Product documentation
- Training manuals
- Policies
- Client documents
- Internal SOPs
Turn document-heavy work into structured workflows.
AI document processing extracts fields from invoices, forms, contracts, and reports. Validation rules and human review help route approved data into the systems your team uses.
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 automation needs clear limits and accountability.
We plan AI workflows with permission controls, data privacy boundaries, human review, audit logs, and a fallback when the system cannot answer confidently.
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
Our eight-step AI automation process tests a complete workflow with your real inputs before production integration.
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 FAQsUsually only the repeatable, well-defined steps. We map the full workflow, automate suitable tasks, and keep human approval where an error would have a material effect.
Yes. A RAG chatbot can retrieve information from your approved website content and knowledge base, then hand uncertain or sensitive questions to a person.
Yes. We can extract structured fields from invoices, contracts, forms, reports, and résumés. We check document and scan quality, validate the output, and route exceptions for review.
Yes, when the CRM provides a suitable API. Depending on the workflow, we can support lead qualification, record updates, call summaries, drafted follow-ups, and pipeline tasks.
Yes. We can assess how knowledge search, summarization, drafting, or classification fits the product you already run, then plan integrations and review controls around its architecture.
We define decision limits, use human review for consequential work, and plan permission controls, audit logs, data privacy boundaries, and a fallback when the model is uncertain.
