Skip to content

LLM Apps & AI Chatbots

Custom AI Chatbot & LLM Integration

Adding an LLM to a product is easy. Making it reliable, safe and affordable at scale is the real work. I integrate Claude, OpenAI and Amazon Bedrock models into web apps, SaaS platforms and messaging channels with structured outputs, guardrails, prompt caching (about 90% input-cost reduction in production) and the monitoring you need to trust it.

At a glance

  • Lower running costs
  • Predictable outputs
  • Safe by default
  • Shipped where users are
Book a free discovery call See related case studies
Outcomes

What you get from this engagement.

  • 01

    Lower running costs

    Prompt caching, model routing and token budgets keep unit economics healthy.

  • 02

    Predictable outputs

    Structured outputs and tool calling instead of fragile free-text parsing.

  • 03

    Safe by default

    Guardrails, moderation and fallback flows for sensitive conversations.

  • 04

    Shipped where users are

    Web, mobile backends, WhatsApp, Slack and voice.

Deliverables

Scope, made concrete.

  • Model selection with cost and latency benchmarks
  • Chatbot or copilot UI and API
  • Prompt engineering, structured outputs and tool calling
  • Prompt caching and token-cost optimization
  • Guardrails, moderation and fallback flows
  • Billing, auth and multi-channel delivery
Use cases

Where it delivers value.

  • Customer-support chatbots
  • AI features inside SaaS products
  • Internal copilots for teams
  • Lead-qualification assistants
  • Subscription AI products

Technology

  • Claude API
  • OpenAI API
  • Amazon Bedrock
  • Google Gemini
  • Hugging Face
  • Ollama
  • vLLM
  • Node.js
  • Python
  • FastAPI
  • Next.js
  • Stripe
Proof

Related case studies.

Real systems delivered with this approach, with the constraints and results that came with them.

whatsapp · ai-sponsor
24/7
0:14

whisper → “Tonight is hard. Can we talk for a minute?”

0:19

I’m here. Let’s take it one step at a time. What’s on your mind?

prompt cache · input tokens≈90% saved

Illustrative interface · client data never shown
01AI Sponsor

Multi-Channel AI Support SaaS Platform

The backend for a 24/7 AI sponsor that supports people in 12-step recovery over chat and WhatsApp voice. It combines Claude-powered conversations, Stripe subscription billing and a prompt-caching architecture that cut LLM input costs by about 90%.

LLM input-cost reduction
~90%
Chat and voice availability
24/7
Automated test categories
12
Read case study
Digital Health4 stages
  1. 01Screen
  2. 02Retrieve
  3. 03Generate
  4. 04Remember

Stage 1, Screen: Crisis-risk detection. Stage 2, Retrieve: Pinecone CBT/mindfulness corpus. Stage 3, Generate: GPT-4o via LangChain. Stage 4, Remember: Session memory and mood history.

02Namhee

Safety-Aware Mental-Health AI Assistant

A RAG-grounded mental-health support assistant with mood assessment and tracking, CBT and mindfulness guidance, session memory and automatic crisis-risk detection that points users to helplines.

Pass rate across 11 core tests
100%
RAG response target
<5s
Crisis-risk escalation
Auto
Read case study
cloudops-agent · amazon bedrock
Confirm-first
Create a private S3 bucket for invoices in Ireland, with versioning.

I’ll create this resource. Please confirm:

Bucket
invoices-archive-eu
Region
eu-west-1
Access
Private, public access blocked
Versioning
Enabled
Confirm this change? (yes/no)Yes, createNo

boto3 call runs only after an explicit yes · claude 3.5 sonnet on bedrock

Illustrative interface · client data never shown
03CloudOps Agent

Natural-Language Cloud Operations Agent on Amazon Bedrock

A chat-driven AWS operations agent on Amazon Bedrock that lets non-engineers create, inspect, update and delete cloud resources in plain language, with validation and confirm-before-execute guardrails on every change.

Changes require explicit approval
100%
Full resource lifecycle in plain language
CRUD
Claude 3.5 Sonnet on AWS
Bedrock
Read case study
civic-assistant · dual endpoint
10K+/day
“When does the youth scholarship program expand?”temporal router → planned

Publicns: public

The expansion is part of the next program cycle. Eligibility criteria and official announcements are listed on the program page.

2 public sources

Field teamns: field

Rollout guidance for coordinators: priority regions, required documents and the household briefing checklist.

3 field sources · restricted

one knowledge base · two namespacesno cross-audience leakage

Illustrative interface · client data never shown
04Civic Intelligence Assistant

High-Volume Public-Sector RAG Assistant

A dual-endpoint RAG assistant that serves the public and the field teams of a national political organization from one governed knowledge base. It is sized for 10,000+ queries a day, with temporal query routing and namespace-isolated retrieval.

Queries per day capacity
10K+
Audience-specific endpoints
2
Governed knowledge base
1
Read case study
How we work

A delivery process built for zero surprises.

Clear milestones, working demos every week and measurable quality gates, so you always know what's done, what's next and what it costs.

  1. 01

    Discover

    A free 30-minute call to understand the problem, the data and what success looks like.

    You getClear problem statement and success metrics

  2. 02

    Architect

    A written technical plan: architecture, model and vector-store choices, risks, milestones and a fixed quote.

    You getArchitecture document and milestone plan

  3. 03

    Build

    Iterative delivery in short milestones with working demos, so you see progress every week, not at the end.

    You getWorking increments you can test

  4. 04

    Evaluate & harden

    Measured against an evaluation set, then hardened with guardrails, access control, error handling and load testing.

    You getEvaluation report and test suite

  5. 05

    Deploy & support

    Dockerized deployment to your cloud, documentation and handover, with optional ongoing support and improvement.

    You getProduction system plus documentation

AI project estimator

Get a tailored project plan in 60 seconds.

Five questions about what you’re building. Instantly see the recommended architecture, delivery phases and a realistic timeline, then email it to yourself.

  • Architecture
  • Phased plan
  • Timeline

Step 1 of 5 · What are you building?

Start the estimator
FAQ

LLM Apps & AI Chatbots: common questions.

Claude, GPT or open-source: which model should we use?

It depends on the task, data sensitivity, latency and budget. I benchmark candidates on your real prompts. Hosted frontier models usually win on quality, while open-source models via Ollama or vLLM win when data must stay on your infrastructure.

How do you keep LLM costs under control?

Prompt caching, smaller models for simple steps, retrieval instead of oversized prompts, token budgets and usage monitoring. On one production SaaS backend, caching alone cut input-token costs by about 90%.

Can the chatbot answer from our own data?

Yes. That is retrieval-augmented generation (RAG): your documents are indexed and the model answers from them with citations, without retraining.

Do you fine-tune models?

When it is the right tool. LoRA/QLoRA fine-tuning helps with consistent style, format or domain language, often combined with RAG for knowledge.

Other services

Available for new engagements

Let’s scope your LLM Apps & AI Chatbots project in 30 minutes.

Free discovery call. You'll leave with a clear view of the architecture, risks and next steps, whether or not we work together. I reply within 24 hours.

Book a free discovery callSend project details

Not ready to talk yet? Get an instant project plan.