Turn everyday workflows into intelligent action.
Menteso builds AI agents that answer questions, work with your business tools, and move tasks forward—with the controls your team needs.
From a focused pilot to connected enterprise workflows, start with one business problem worth solving.
Discuss Your AI Project →Explore What We BuildYour team is busy. But is the work moving forward?
An unanswered inquiry, a missing document, or another copy-and-paste update can look small in isolation. Across your team, those interruptions create queues, rework, and missed opportunities.
Your best people are doing repetitive work
Skilled staff spend their day searching inboxes, entering the same information twice, and answering familiar questions. Higher-value work keeps getting pushed to tomorrow.
Customers wait while teams catch up
A prospect asks a question after hours. A customer repeats their problem to another department. Slow handoffs can turn a straightforward request into frustration—or a lost conversation.
Your information is spread across tools
The answer may exist in a document, CRM record, or support ticket, but finding it depends on knowing whom to ask. Work stalls whenever the right person is unavailable.
Rigid automation breaks at the exceptions
A fixed rule can handle a predictable form. A request with missing details or unfamiliar wording needs context. Without a way to clarify or escalate, exceptions pile up.
More volume means more pressure on headcount
When every additional request needs another manual touch, growing the business also grows the workload. Hiring alone may leave the underlying bottleneck unchanged.
Managers see the backlog too late
When progress lives in messages and spreadsheets, it is hard to distinguish completed work from work waiting for a decision. Problems become visible only after a deadline slips.
Which bottleneck costs you the most?
Start with a workflow your team handles repeatedly. Measure its volume, time per task, rework, and waiting time—then assess where an agent could help.
Discuss Your Biggest Bottleneck →Give work a next step—even when the request is unstructured.
An effective agent combines context, approved information, and defined actions. We design the workflow so routine tasks can progress and exceptions reach the right person.
Understand the request
Interpret a question, email, or document, identify what is needed, and ask for missing information before starting the next step.
Find the relevant evidence
Retrieve information from approved sources so staff can review an answer in context rather than search across multiple applications.
Take permitted action
Prepare records, route requests, or perform approved updates through connected tools. Sensitive actions can stay behind a human approval step.
Improve through reviewed feedback
Use evaluation results and user feedback to refine prompts, knowledge sources, and workflows. Changes are reviewed and tested rather than assumed to improve automatically.
AI agents designed around real work.
Choose a practical starting point. We scope the data, integrations, and success measures before development begins.
Customer Support Agents
Answer questions from approved help content, assist with common requests, and route complex issues to a person with context.
Sales & Lead Qualification Agents
Capture requirements, qualify inbound inquiries, prepare CRM updates, and support follow-ups with your team’s approval.
Knowledge & Research Copilots
Help employees find answers across permitted documents and knowledge bases, with source references and access-aware retrieval.
Document Processing Agents
Extract and structure information, prepare summaries, and flag missing details for review across document-heavy workflows.
Workflow & Operations Agents
Connect tasks across CRM, service desks, internal applications, and APIs, with clear permissions and exception handling.
Custom & Multi-Agent Systems
Build specialized assistants and coordinated workflows when a single agent is insufficient, with observable steps and defined responsibilities.
From a single assistant to connected AI workflows.
Select the capabilities that solve the problem. Discovery determines whether an agent, a simpler automation, or a combination is the right fit.
Custom AI Agent Development
Design task-specific assistants around your operating rules, users, data, and systems. Define the agent’s role, tools, boundaries, and handoff behavior before extending its scope.
AI Consulting & Strategy
Prioritize potential use cases, assess data readiness, and compare implementation effort with expected value. Leave discovery with a scoped starting point and measurable acceptance criteria.
AI Chatbot Development
Create conversational experiences for support, intake, and guided inquiries. Connect answers to approved content and preserve context when a conversation moves to your team.
Machine Learning Solutions
Assess whether prediction, classification, or recommendations can improve a workflow. Model feasibility depends on available data, quality, and a meaningful evaluation baseline.
AI Analytics & Reporting
Turn permitted business data into summaries and operational views. Help teams investigate trends and exceptions while preserving a route back to the underlying records.
Language & Document Intelligence
Extract fields, classify text, summarize documents, and support knowledge search. Use structured outputs and review steps where mistakes could affect business decisions.
Intelligent Workflow Automation
Coordinate work between applications through available APIs. Define validation, retries, approvals, and exception queues so a failed step does not silently lose a request.
Pick a team. See the workflow.
Click a scenario to see a sample request, response, and the steps behind it.
“Where is my order, and can you change the delivery address?”
I can check the order after verification. An address change needs the service team’s approval, so I’ll prepare the request for review.
Resolve the routine. Route the exception.
Look up permitted order information, separate the status question from the change request, and hand off with context.
Illustrative, scripted examples—not live AI responses or client results.
How much time goes into this one task?
Move the sliders to explore a workload scenario. Use the estimate to start a discussion about where automation might help.
Choose your own assumption. Actual automation potential must be established in a pilot.
From an estimated 83.3 hours of current task time.
Tasks × minutes ÷ 60 × assumed share. Excludes build time, reviews, exceptions, maintenance, and operating costs. This is a planning estimate, not a savings promise.
Assess This Workflow →A clear path from idea to a working agent.
Bring us a repetitive process, a growing support queue, or information your team struggles to find. We will help define a manageable first release.
Plan Your First Agent →Your discovery discussion covers
- The workflow and the people using it
- Available data and integration requirements
- What the agent can do and when it must ask
- How quality, cost, and business value will be measured
- A scoped implementation approach and estimate
Scope, pricing, and delivery dates are agreed after requirements review.
Build, validate, then expand.
Discover & prioritize
Map the current workflow and define the first use case, baseline, and acceptance criteria.
Design & connect
Select the model approach, knowledge sources, tools, access boundaries, and approval steps.
Develop & evaluate
Build the agent and test realistic tasks, unsupported requests, failure cases, and escalation paths.
Pilot with your team
Run a controlled release with users and compare results against agreed quality and cost measures.
Deploy & enable
Connect the approved production systems, document operating procedures, and train your team.
Monitor & improve
Review errors, feedback, usage, latency, and costs under an agreed support arrangement.
Choose the stack around the job—not the other way around.
Model quality, operating cost, latency, data handling, and deployment needs all affect the architecture. Options below are evaluated for suitability; inclusion does not imply a vendor partnership.
Language models
OpenAI, Anthropic Claude, or suitable open-weight models, selected against representative tasks and your data requirements.
Agent orchestration
Frameworks such as LangChain or LangGraph, or a simpler custom workflow where it offers better control and maintainability.
Knowledge retrieval
Document processing, embeddings, vector search, and source references, with access rules applied to the information an agent can retrieve.
Tools & integrations
Application APIs and MCP-compatible tools where appropriate, with explicit permissions, validated inputs, and auditable actions.
ML & local model options
Evaluate Hugging Face tooling, PyTorch, TensorFlow, or Ollama where training, inference, or local deployment requirements justify them.
Deployment & monitoring
Choose hosting, secrets management, logging, evaluation, and cost controls to match your environment and agreed support model.
A development engagement built around your business outcome.
A convincing demo is a starting point. A useful business system also needs integration, ownership, clear limits, and a way to measure whether it works.
A business-first scope
Begin with the task you want to improve, who owns it, and what a successful result looks like. Avoid paying to automate a poorly defined process.
Your workflow shapes the solution
Design the agent around your terminology, escalation rules, and review requirements instead of forcing staff into a generic conversation flow.
Integration planned from the start
Identify system access and API limitations early. Separate what can be delivered in the pilot from dependencies that need additional work.
Controls you can understand
Agree on what the agent can see, what it can change, and when it must stop for approval. Document those decisions for the people operating it.
A measured path to expansion
Test a focused use case first. Expand based on observed task quality, user adoption, operating cost, and the remaining failure modes.
A clear operational handover
Define documentation, team enablement, maintenance responsibilities, and support scope so the system has an owner after launch.
Useful automation. Clear boundaries.
We define controls around your use case and deployment requirements, rather than treating autonomy as an all-or-nothing choice.
Permissions & approvals
Limit tools and data access by role. Require human approval before consequential actions.
Grounded answers
Use approved knowledge sources, references where appropriate, and explicit fallback behavior when evidence is missing.
Evaluation & visibility
Review task results, failures, latency, and operating costs. Keep logs appropriate to your privacy and retention requirements.
Make your existing tools work together.
Integration feasibility is assessed during discovery. Depending on available APIs and permissions, agents can work with CRM platforms, document repositories, support systems, databases, and custom software.
Practical use cases across teams
- Legal & IP: intake, document organization, and research support for professional review
- Sales & services: inquiry routing and CRM assistance
- Ecommerce: product questions and support triage
- HR & operations: policy search and internal requests
- Technology teams: knowledge retrieval and service desk assistance
Find the workflow inside your industry.
These are potential applications to assess during discovery. Suitability depends on your data, systems, risk requirements, and professional review obligations.
Legal & Intellectual Property
Organize intake, assemble matter information, and retrieve approved knowledge. Keep legal judgment and filing decisions with qualified professionals.
Banking & Financial Services
Assist with internal knowledge search, document triage, and service requests. Keep financial decisions and regulated advice under authorized review.
Healthcare & Life Sciences
Support administrative inquiries, appointment workflows, and document organization with appropriate access controls. Clinical decisions remain with clinicians.
Retail & Ecommerce
Answer product questions from approved content, assist with order-status requests, and route returns or exceptions to the service team.
Manufacturing
Help staff locate operating procedures, organize maintenance requests, and summarize incident records for the responsible team.
Logistics & Supply Chain
Collect shipment details, retrieve available status information, and flag delivery exceptions for operations staff.
Education & Training
Guide administrative inquiries, help users find course information, and assist staff with knowledge retrieval and content preparation.
Insurance
Collect initial information, organize documents, and support policy-content search. Coverage and claims decisions stay with authorized reviewers.
Real Estate
Qualify property inquiries against stated criteria, organize documents, and help coordinate viewing requests.
Human Resources
Answer questions from approved policy documents, route employee requests, and organize onboarding tasks without making hiring decisions.
Travel & Hospitality
Handle common service questions, collect guest preferences, and prepare booking-related requests for confirmation.
Telecommunications
Help with service information, support intake, troubleshooting guidance, and escalation summaries based on approved procedures.
Government & Public Services
Guide users to published information, help organize service requests, and support internal document workflows with defined access and review.
What a support agent could change in a busy ecommerce team.
Imagine a team answering the same product and delivery questions while complex cases wait. Here is how a scoped agent workflow could address that bottleneck.
A queue of repeat questions
Agents search product pages, check order records, and write similar replies. Customers wait, and staff have less time for exceptions that require judgment.
Answer, check, or escalate
An assistant retrieves approved product information. After identity and permission checks, it can look up available order status. Refunds, disputes, and uncertain answers go to a person.
Prove value in the pilot
Compare first-response time, resolution accuracy, escalation quality, customer feedback, and cost per completed request against the current process.
This is an illustrative solution scenario, not a Menteso client case study. No performance percentages or savings are claimed.
Explore a Similar Workflow →Before you build.
What is AI agent development?
It is the design and implementation of software that interprets requests, uses relevant information, and carries out defined steps through permitted tools. The project includes integration, evaluation, access controls, and operating procedures.
Which AI model should we use?
We assess representative tasks against quality, latency, cost, deployment, and data handling requirements. A project may use a hosted model, an open-weight model, or a combination. The choice is made during solution design.
How will you protect our business data?
During design, we agree on access permissions, model-provider data handling, encryption needs, retention, and deployment requirements. Controls are implemented and tested against that scope; any specific compliance requirement must be assessed explicitly.
How is an AI agent different from a chatbot?
A chatbot typically focuses on conversation. An agent can also retrieve permitted information and use connected tools to complete defined tasks. The level of autonomy depends on the workflow and controls you approve.
Can you integrate with our existing software?
We assess the APIs, authentication options, data quality, and permissions of your systems during discovery. Where an integration is feasible, we define its scope and approval requirements before implementation.
How long does development take and what does it cost?
Timing and cost depend on the number of workflows, integrations, data preparation needs, deployment requirements, and acceptance criteria. We provide an estimate after reviewing your requirements.
Can the agent use our internal documents?
Where authorized, we can design retrieval over selected documents and knowledge bases. Access rules, retention, model-provider data handling, and hosting requirements are agreed during design.
Will the AI always provide the right answer?
No AI system is error-free. We reduce risk through task-specific evaluations, source grounding, clear limitations, escalation paths, and human review for important decisions.
Do you offer ongoing support?
Ongoing monitoring, maintenance, and improvements can be included in a support arrangement with an agreed scope. Model usage, hosting, and third-party software costs are clarified in the proposal.
Stop adding manual steps to a growing workload.
Choose one process where delays, repeat questions, or handoffs are holding your team back. Let’s assess what an agent can realistically take off their plate.
What would you like your team to stop doing manually?
Tell us about the workflow, the tools you use, and the outcome you want. We’ll discuss a practical starting point.
In your message, include your company, use case, existing systems, and preferred timeline. Please avoid sharing confidential documents or credentials.