Generative AI systems built to ship -
not stay a demo
RAG pipelines, AI agents, custom GPT and LLM integration, and production AI backends - designed with guardrails, fallbacks, and evals so they hold up in real use, not just a notebook.
4 Generative AI specialisations.
Production-grade from day one.
From document-grounded RAG systems and autonomous AI agents to custom GPT integrations and the backend infrastructure that keeps them reliable - every AI system we ship is built for real use, not a weekend demo.
RAG Systems & Document Intelligence
Retrieval-augmented generation pipelines that ground LLM answers in your own documents, knowledge base, or database - with vector search, chunking strategy, and citation tracking built in.
AI Agents & Workflow Automation
Tool/function-calling agents that take real actions across your stack - with guardrails, fallbacks, and human-in-the-loop checkpoints where it matters.
Custom GPT & LLM Integration
OpenAI and Claude integration, custom GPTs, prompt engineering, and chatbot interfaces wired directly into your existing product or CRM.
AI App Backend & Deployment
Production backends for AI features - rate limiting, caching, token cost control, monitoring, and evals so reliability doesn't depend on luck.
CRM & Zoho AI Integration
AI-enhanced workflows inside Zoho and other CRMs - Zoho Analytics dashboards, AI-assisted data entry, and intelligent lead routing.
AI systems that work in production, not just in a notebook
Most AI prototypes fail the moment real users touch them. We build for the failure cases first - rate limits, hallucinations, bad inputs, and cost overruns - so your AI feature still works on a bad day.
Guardrails & Fallbacks
Every AI feature ships with input validation, output checks, and a graceful fallback path when the model gets it wrong.
Evals & Reliability Testing
Structured evaluation sets and reliability testing before launch - not vibes-based "it worked when I tried it."
Production-Grade RAG
Vector database architecture, chunking strategy, and retrieval tuning built for accuracy, not just a quick demo.
Full Code Ownership
Every prompt, pipeline, and integration belongs to you - full source code delivery, no vendor lock-in.
From prototype to production AI, in a clear 4-step process
Generative AI projects fail when reliability is an afterthought. Our process treats evals, guardrails, and cost control as first-class steps, not a cleanup pass at the end.
Scope · Build · Evaluate · Ship
Define the use case and audit your data
We map your data sources, success criteria, and failure modes before choosing a model, architecture, or vector database.
Build the pipeline, agent, or integration
RAG pipelines, agent tool-calling, or LLM integration built with prompt versioning and structured outputs from the start.
Run evals and add guardrails before launch
Structured evaluation sets, edge-case testing, and fallback paths added before any real user sees the system.
Ship to production with cost and quality monitoring
Token cost tracking, latency monitoring, and ongoing eval runs so quality doesn't quietly degrade after launch.
AI Development Services - Frequently Asked Questions
Answers to the questions US & UK clients ask us most.
What generative AI services does DapperSolutions actually offer?
Four specialisations: RAG systems and document intelligence, AI agents and workflow automation, custom GPT and LLM integration, and AI app backend and deployment. As a generative AI development company we scope each project around the specific specialisation your use case needs, not a one-size-fits-all package.
How do I know which AI service fits my project - RAG, an agent, or an integration?
If you need answers grounded in your own documents or knowledge base, that's RAG. If you need a system that takes multi-step actions across your tools, that's an agent. If you just need a chatbot or AI feature wired into your existing app or CRM, that's LLM integration. Most production systems combine more than one - we map this out during scoping, not after you've committed to the wrong architecture.
Do you quote a project cost upfront, or bill hourly?
We scope the use case, data sources, and success criteria up front, then quote a project cost so there are no open-ended hourly surprises. Book a free consultation and we'll give you a number before any work starts.
How long does a typical generative AI project take to go live?
It depends on the specialisation and how much of your data and systems need integration work, but our process always runs through the same four stages - scope and data audit, build, evaluate and harden, then deploy and monitor. Simple integrations can ship in weeks; multi-component systems with agents and RAG take longer because evals and guardrails aren't skipped to hit a date.
What happens after my AI feature goes live - do you just hand it off?
No. Every system we ship includes token cost tracking, latency monitoring, and ongoing eval runs so quality doesn't quietly degrade after launch. You also get full code ownership - every prompt, pipeline, and integration is yours, with no vendor lock-in if you want to bring maintenance in-house later.