Knowledge-grounded answers
Retrieve from approved policies, catalogs or internal documents and show the source of an answer.
03 / Intelligent workflows
Give repetitive work a better first pass.
AI becomes valuable when it understands your context, uses approved information and knows when a person should decide. N9 designs agents and automations around actual business tasks—answering, drafting, classifying, routing and taking bounded actions—with visibility into what happened.
Inside the product
The pieces that make it useful.
Features are selected around the users and the outcome, not added just to fill a feature list. These are some of the building blocks a ai & automation project might include.
Structured intake
Turn emails, forms or documents into consistent records for review.
Action tools
Allow an agent to check status, create a task or draft a response through controlled APIs.
Human handoff
Pass conversation history and the reason for escalation to the right person.
Approval gates
Require human review for sensitive, financial or irreversible actions.
Workflow orchestration
Connect triggers, rules, queues and follow-ups across systems.
Evaluation and feedback
Test important scenarios, review failure patterns and refine behavior over time.
Activity history
Record what the automation saw, decided, proposed and completed.
What we could build
Products with a real job to do.
Every example can be scoped to your business, your data and the first release that creates value. These are possibilities, not claims about completed client projects.
Customer support agent
Answer routine questions, check approved systems and escalate difficult cases.
Sales qualification agent
Ask useful questions, update CRM records and help schedule the right next conversation.
Document processing workflow
Extract fields from incoming documents and flag uncertain values for review.
Operations exception assistant
Summarize delays, missing data or anomalies and suggest the next action.
Internal knowledge assistant
Help employees find policy and process answers with citations to approved sources.
Back-office automation
Draft follow-ups, reconcile routine data and move work between systems under rules.
Example blueprint
An agent that works with your team, not around it.
Consider a support agent for a growing business. Its job is not to sound clever; it is to resolve the simple cases, gather context for hard cases and avoid making promises it cannot keep.
- 01Connect a curated knowledge base and define the questions the agent can answer.
- 02Give it read-only tools for order or account status, with strict access checks.
- 03Route refunds, exceptions and uncertain answers to a human with full context.
- 04Review outcomes and improve instructions, data and handoff rules using real cases.
How we build
Languages & technology.
We choose the stack around the product, the existing systems, performance needs and who will own it long term. These are examples of technologies we can use—not a promise that every project needs every tool.
Agent logic
Python or TypeScript for orchestration, tool calling, validation and service integration.
Models and retrieval
Model APIs, embeddings or search chosen for quality, privacy, latency and cost.
Business systems
SQL, APIs, event queues and permissions to connect the agent to real workflows.
Quality and safety
Scenario tests, logging, rate limits, approval checkpoints and monitoring.
The final architecture and technology choices are agreed during discovery.
Have a version of this in mind?
Tell us what people need to do, what is slowing them down and what a useful first release would change.
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