AI assistants & agent workflows
Build assistants that answer from approved information and agents that carry out defined steps using permitted tools. Give people review points, source references, and a way to handle exceptions.
Practical examples for your business.
01
Incoming requests → the right queue
Requests are classified by type and urgency and routed to the right queue, and unclear ones are left for a person.
The problem: Someone reads each email or ticket and decides where it goes.
02
Free text → structured fields
The details are extracted into fields in your ERP, CRM or ticketing system, for someone to confirm where needed.
The problem: Part numbers, quantities and dates are copied out of emails and notes by hand.
03
Long threads → short summaries
A short summary of the thread or job notes is added to the record.
The problem: The next person on a case has to read the whole thread to catch up.
A practical approach
Connect the tools your team already uses.
The right starting point depends on your people, information, and business goals. We agree the scope together.
- Common systems: Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini, Azure AI Language, AWS Comprehend
- Classify messages by type, urgency or topic
- Extract specific details from free text
- Summarize long threads and notes
- Keep a person in the loop for uncertain results
- Agree on data access and review steps before implementation
- Define a clear scope, price, and handoff together
Systems around the workflow
Build around your existing nlp & text ai tools.
Sort and summarize emails, tickets and notes, and pull out the details your systems need. We identify the source of truth, permitted access, field mapping, and a safe way to handle failed or repeated requests.
- Azure OpenAI
OpenAI- Anthropic Claude
- Google Gemini
- Azure AI Language
- AWS Comprehend
The records and rules
Use approved knowledge, document versions, permitted customer context, and the language tasks needed for the selected workflow.
The staff decisions
Define when an answer needs source references, clarification, staff approval, or escalation. Access rules apply to retrieved information as well as final responses.
What the pilot should prove
Test ambiguous wording, conflicting or missing sources, unsupported questions, restricted records, misleading input, and a handoff that preserves useful context.
When a connection fails
Keep a traceable record of what was requested, what completed, and what needs review. Agree retry limits, duplicate protection, staff fallback, and the owner who resolves a failed update before launch.
Everyday business, practical AI
See the complete workflow.
These are proposed implementations we can scope around your business. Explore the steps, the staff decisions, and a sensible first pilot.
Customer service centers
Customer service AI grounded in your data
Give customers answers based on your approved help articles, product documentation, and policies. Connect authenticated account lookups where needed, and hand difficult cases to a person with the conversation and relevant context attached.
Customer service centers
Customer service AI grounded in your data
Give customers answers based on your approved help articles, product documentation, and policies. Connect authenticated account lookups where needed, and hand difficult cases to a person with the conversation and relevant context attached.
From request to completion
- 1A customer asks a question through website chat or an existing support channel.
- 2The assistant searches current approved knowledge and checks whether the request needs account verification.
- 3It answers from the relevant sources or uses a narrowly permitted lookup to retrieve that customer’s status.
- 4Missing information, complaints, or requests outside its authority create a ticket for the correct team.
- 5The receiving agent gets the conversation, sources, attempted steps, and a clear next action; staff review gaps in the knowledge base.
Your team stays in control
Keep customer records isolated. Require approval for refunds or contractual commitments. Define what happens when sources conflict, a connection fails, or a customer asks for a human.
What connects
Help center, approved documents, support platform, customer identity, and scoped account APIs. Connecting company knowledge usually means retrieval and configuration; model fine-tuning is a separate decision.
A practical first pilot
Begin with a reviewed set of frequent questions and a limited customer group. Test outdated policies, missing answers, attempted access to another account, and successful staff handoff.
How to evaluate it
Check answer accuracy, successful resolution, repeat contacts, escalation quality, and customer feedback. A conversation ending is not proof that the issue was resolved.
Growing teams & enterprises
Searchable company knowledge for employees
Help employees find current procedures, product information, and internal answers across approved sources. Answers link back to the source and respect the employee’s access.
Growing teams & enterprises
Searchable company knowledge for employees
Help employees find current procedures, product information, and internal answers across approved sources. Answers link back to the source and respect the employee’s access.
From request to completion
- 1An employee asks a question in an internal assistant.
- 2The system checks identity and permitted knowledge sources.
- 3Search retrieves relevant current documents and the assistant prepares an answer with references.
- 4Missing or conflicting information routes to the document owner instead of becoming an invented answer.
- 5Reviewed gaps inform documentation updates and the assistant’s evaluation set.
Your team stays in control
Honor document permissions and retention requirements. Separate teams or customers where needed and remove access when the source or user permission changes.
What connects
Company identity, document repositories, help content, permission-aware search, and an internal interface.
A practical first pilot
Start with one team and a curated source collection. Test permission boundaries, old versions, conflicting documents, and unknown questions.
How to evaluate it
Check answer accuracy, source relevance, time to find information, and unresolved knowledge gaps.
Implementation, end to end
A working process your team can operate.
01
Understand the current work
Walk through a real request with the people doing the job. Identify the source records, decisions, bottlenecks, and exceptions before choosing software.
02
Build a useful pilot
Define a limited starting scope and create a working version. Test representative requests alongside difficult cases, missing data, and failed connections.
03
Launch with clear ownership
Agree access, approval steps, acceptance checks, and a release plan. Show staff how to use the system, review exceptions, and contact the responsible owner.
04
Maintain and improve
Provide the agreed documentation and handoff. Review usage, operating costs, and issues; define support coverage and future changes in the engagement.
Connect the rest of the workflow.
Data foundations & AI readiness
Organize the data and knowledge behind a priority workflow. Identify authoritative sources, clean up agreed records, document ownership, and prepare searchable information or reliable reporting inputs before adding more automation.
Before we begin
The questions that shape your project.
Bring the business problem. We can help translate it into a practical technical plan.
Can you work with our existing software?
We review the systems you use, available APIs or supported imports, subscription requirements, and access permissions. The scope identifies which connections can be implemented and which need an alternative.
What do you need from our team?
A process owner, examples of the current work, and access to the systems included in the scope. Your staff help confirm the business rules, review a pilot, and decide whether the result is ready to use.
How are cost and success agreed?
The proposal defines deliverables, assumptions, responsibilities, acceptance checks, and pricing. Third-party subscriptions and usage costs are identified separately. We choose meaningful measures from your existing process rather than promise an untested result.
What happens after launch?
The agreement defines code and account ownership, documentation, training, deployment responsibilities, and any ongoing support. Staff get a clear way to handle failures and requests for changes.
Let’s talk about what you want to improve.
A short conversation can help you decide where AI and better systems would make a useful difference.