Service

Custom AI Agents

AI teammates that think, decide, and act across your entire stack.

We design and build purpose-built AI agents for your exact business context — customer support, internal operations, research, sales assistance, and more — each with long-term memory, tool access, and the ability to take real actions across your software.

70%

Reduction in support ticket volume

24/7

Availability without additional staffing

<2s

Average agent response time

85%+

First-contact resolution rate

Business Problems We Solve

Is Your Business Dealing With Any of These?

These are the real operational problems our clients come to us with. If any of these sound familiar, we have a direct solution.

Generic chatbots giving templated, irrelevant responses

Customer queries taking hours to resolve during off-hours

Support team overwhelmed at peak times with no overflow

No AI system that understands your product, policies, and context

Manual internal research consuming your best people's time

Unable to scale operations without proportional headcount increases

How We Build This

Our Exact Implementation Process

Not theory. Not templates. This is the precise delivery process we follow for every Custom AI Agents engagement.

1
Phase 1Agent Design
Define agent persona, scope, and decision boundaries
Map all use cases, intents, and expected user interactions
Design conversation flows and fallback paths
Define what the agent can and cannot do — hard guardrails
2
Phase 2Knowledge Base Build
Collect product docs, SOPs, FAQs, and policies
Structure and chunk documents for optimised RAG retrieval
Build vector database with semantic search indexing
Evaluate retrieval quality with relevance scoring tests
3
Phase 3Agent Development
Build core agent logic using LangGraph / CrewAI
Implement short-term and long-term memory systems
Connect tool integrations: CRM, calendar, email, databases
Build escalation routing for complex or sensitive queries
4
Phase 4Prompt Engineering
System prompts tuned precisely to your brand voice
Chain-of-thought reasoning for multi-step queries
Anti-hallucination guardrails and confidence thresholds
Multi-turn context window management and compression
5
Phase 5Evaluation & Testing
Red-team testing across edge cases and failure modes
Response accuracy benchmarking against golden dataset
Latency optimisation targeting under 2s response time
Human evaluation panel before production deployment
6
Phase 6Deployment & Monitoring
Deploy via API, web widget, Slack, or WhatsApp
Real-time conversation monitoring dashboard
Automated flagging of low-confidence responses
Continuous fine-tuning from conversation log analysis
Technologies We Use

The Stack Behind Your Solution

Every technology in our stack is chosen for a specific reason — not because it is trendy.

OpenAI GPT-4o

Primary reasoning and conversation engine

Claude 3.5 Sonnet

Long-context and nuanced understanding

LangGraph

Stateful multi-step agent orchestration

CrewAI

Multi-agent collaboration frameworks

pgvector / Pinecone

Vector storage for RAG knowledge retrieval

Supabase

Agent memory and conversation persistence

Redis

Session caching and real-time state management

Next.js

Agent chat UI and admin monitoring dashboard

Deliverables

Everything You Receive

We define deliverables at the start of every engagement — not at the end. You know exactly what you are getting before a single line of code is written.

Production-deployed AI agent (API + UI)
Knowledge base built from your company data
Admin dashboard with conversations, analytics, fallbacks
Integration with your existing tools and workflows
Full source code and infrastructure configuration
Prompt library and tuning documentation
Team training on agent management and updating
90-day performance monitoring and optimisation
Project Timeline

From Kickoff to Launch

A realistic, phase-by-phase timeline so you can plan resources, stakeholder reviews, and launch milestones with confidence.

Week 1

Agent Design & Use Case Mapping

Week 2

Knowledge Base Build

Weeks 3–4

Agent Development & Tool Integrations

Week 5

Evaluation & Red-Teaming

Week 6

Deployment & Team Training

Why Choose N-CYPHER

What Makes Our Approach Different

We design the agent's decision boundaries before writing a single line of code

Our RAG pipelines are evaluated for retrieval quality, not just built and shipped

Every agent includes escalation logic — we account for what AI shouldn't handle

Agents are trained on your exact language, policies, and product knowledge

Industries We Serve

Built for Your Sector

We have delivered Custom AI Agents for clients across these industries — with domain context already built into our approach.

E-commerce & DTCSaaS & TechnologyHealthcare & TelehealthFinancial ServicesLegal & ComplianceEducation & EdTechReal EstateTravel & Hospitality
Frequently Asked Questions

Common Questions Answered

How is this different from a basic chatbot?

What data do you use to build the knowledge base?

How do you prevent the agent from hallucinating wrong answers?

Can the agent be deployed inside our existing product?

Ready to Get Started?

Book a free discovery call. We will audit your current setup, define the right scope, and give you an honest proposal — no obligation.