Purpose-built AI agents that handle customer inquiries, analyze data, orchestrate workflows, and more.
Getting started
We begin with stakeholder interviews to understand your workflows, pain points, and desired agent capabilities. We then define the agent's persona, audit your knowledge base, and assess risks before designing the architecture.
What's included
Stakeholder interviews and use case definition
Agent persona, tone, and behavioral boundary design
Knowledge base audit and RAG pipeline development
Prompt engineering with guardrails and safety layers
Red team testing and adversarial validation
30-day hypercare with conversation analytics and optimization
How we deliver
A structured, phase-gated process. Every phase includes formal sign-off before proceeding to the next.
01
Requirements & Use Case Definition
1–2 weeks
We interview your stakeholders, define each agent's purpose and success criteria, establish its personality, and audit the knowledge it will need access to.
What we do
Stakeholder interviews to understand workflows and pain points
Use case documentation with scope, inputs, outputs, and success criteria
Persona and tone definition with brand alignment
Knowledge base audit of existing docs, FAQs, and data sources
Risk and ethics assessment for failure modes and guardrails
What you get
Agent use case specification
Agent persona profile and tone guidelines
Knowledge base inventory with access requirements
Risk and ethics report with mitigation strategies
02
Architecture & Prompt Design
1–2 weeks
We design the agent framework — model selection, tool integrations, prompt engineering, guardrails, and conversation flow mapping.
What we do
Architecture design with model selection and memory strategy
Prompt engineering with chain-of-thought templates
Tool and API design with function-calling schemas
Guardrail framework with content filtering and escalation triggers
Conversation flow mapping with edge cases and handoff scenarios
What you get
Agent architecture document with component specs
Versioned prompt library with testing notes
Tool and API specification
Guardrail policy document and escalation matrix
Conversation flow diagrams with branching logic
03
Development & Training
2–4 weeks
We build the core agent, ingest your knowledge base, connect to your systems, create evaluation benchmarks, and iteratively tune prompts.
What we do
Core agent build with model integration and state management
Knowledge ingestion via RAG pipeline or fine-tuning preparation
Integration development with your CRM, helpdesk, and databases
Automated evaluation suite for accuracy, relevance, and safety
Iterative prompt tuning based on eval results
What you get
Functional AI agent in staging environment
Indexed and retrievable knowledge base
Working API integrations with error handling
Evaluation report with benchmark scores
Prompt version history with performance deltas
04
Testing, Validation & Launch
2–3 weeks
Adversarial red team testing, stakeholder beta, performance benchmarking, production deployment, and team training.
What we do
Red team testing for prompt injection and abuse scenarios
Stakeholder beta with structured feedback collection
30 days of active monitoring, conversation analytics, knowledge base updates, and a path to ongoing managed service.
What we do
30-day hypercare with rapid bug fixes and behavioral adjustments
Conversation analytics for quality and improvement opportunities
Knowledge base updates as new information becomes available
Model and prompt upgrades as better models release
Transition to managed service or retainer
What you get
Monthly performance report with quality scores
Continuous improvement log with impact measurements
Quarterly strategy review for agent expansion
Managed service agreement with SLA and pricing
Frequently Asked Questions
A chatbot typically follows pre-scripted conversation flows. An AI agent is powered by large language models and can understand context, reason about problems, access tools and data, and take actions autonomously. Our agents are far more capable than traditional chatbots.
Yes. We build agents that connect to your CRM, databases, email, helpdesk, and other tools via APIs. If your software has an API, we can integrate it. We document all connections during the architecture phase.
We use retrieval-augmented generation (RAG) to ground responses in your actual data, plus guardrails including content filtering, hallucination detection, and human-in-the-loop gates. We also run adversarial red team testing before launch.
Pricing varies based on complexity, integrations, and scope. We provide a detailed Statement of Work with fixed or estimated pricing after the discovery phase. Every engagement includes a clear scope, timeline, and milestone-based delivery.
Typically 6-10 weeks end-to-end: 1-2 weeks for requirements, 1-2 weeks for architecture, 2-4 weeks for development, and 2-3 weeks for testing and launch, followed by 30 days of hypercare.
See It in Action
Real-world examples of how businesses use this service to drive results.