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Generative AI Solutions That Ship to Production, Not Just Demos

We design and ship generative AI features that earn their place in real products — LLM copilots, RAG search, multimodal experiences, and agentic workflows. Built with evaluation pipelines, cost controls, and human-in-the-loop guardrails, so the AI keeps performing after launch.

Quick answer

Generative AI development means building production systems on top of foundation models — GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.3 — extended with retrieval-augmented generation (RAG), fine-tuning on domain data, multimodal inputs, and agentic workflows. Unico Connect ships generative AI features across fintech, healthcare, education, and SaaS in 25+ countries. Every project includes evaluation pipelines, cost controls, and human-in-the-loop guardrails so production AI keeps performing after launch — not just in the demo.

LLM copilot interface
RAG knowledge retrieval pipeline
Multimodal generative AI experience
LLM copilot interface
RAG knowledge retrieval pipeline
Multimodal generative AI experience
LLM copilot interface
RAG knowledge retrieval pipeline
Multimodal generative AI experience
LLM copilot interface
RAG knowledge retrieval pipeline
Multimodal generative AI experience

From Demo to Production — How We Build Generative AI Differently

Most generative AI projects look impressive in the demo and fall apart in production. We've built a delivery model that closes that gap — evaluations, guardrails, and operational maturity from day one.

Typical GenAI Project

Single prompt engineered in a notebook

A working prototype on cherry-picked examples that breaks on real production inputs and edge cases.

No evaluation harness

Changes to prompts or models are tested by hand, which means regressions ship silently and accuracy drifts unnoticed.

Generic foundation model, no retrieval

Hallucinations on domain-specific questions because the model has no grounding in your actual product, documentation, or data.

Cost runs away after launch

Token spend balloons as usage grows because there is no budget per request, no caching, and no multi-model routing.

No fallback when the model fails

When inference is slow, rate-limited, or wrong, the user-facing experience just breaks — no graceful degradation, no human handoff.

Engineers Validated

Production-Grade GenAI (How We Build)

Versioned prompts in source control

Every prompt is a tracked artefact with a changelog, A/B tested before promotion. Rollbacks take seconds, not days.

Evaluation pipelines with golden test sets

LangSmith / Promptfoo / custom evals run on every change. We catch regressions and accuracy drift before users do.

RAG grounded in your data

Domain answers come from your documents, not the model's training set. Citations are surfaced so users (and reviewers) can verify.

Cost controls and multi-model routing

Cheap models handle easy queries; premium models handle hard ones. Per-request budgets and caching keep token cost predictable.

Guardrails, fallbacks, and human-in-the-loop

Confidence thresholds, content filters, and escalation paths to human reviewers — AI never makes irreversible decisions alone.

Generative AI Capabilities

LLM-Powered Chatbots & Copilots

Production chatbots, support copilots, and embedded assistants. Memory, tool use, retrieval, and graceful fallback architecture — built for daily use, not demo screenshots.

RAG & Knowledge Retrieval

Document ingestion, chunking, embeddings, vector indexing (Pinecone, Weaviate, pgvector), and reranking. Production-grade retrieval that scales beyond the proof of concept.

Fine-Tuning & Custom Models

Fine-tune open-source models (Llama 3.3, Mistral, Qwen) for cost reduction, domain adaptation, or IP control. Closed-model fine-tuning where supported.

Multimodal AI — Vision, Text, Audio

Vision-language (GPT-4o, Claude 3.5, Gemini 1.5), text-to-image (Imagen, FLUX, Stable Diffusion), and speech (Whisper, ElevenLabs). Combined for richer product experiences.

Content Generation — Text, Image, Code

Generative AI features inside real products: long-form drafts, image generation, code completion, document summarisation. Quality controls and edit suggestions baked in.

Agentic Workflows with Tool Use

Production agents using LangGraph, OpenAI Agents SDK, and Anthropic tool use. Multi-step plans, MCP servers, and human-in-the-loop checkpoints for high-stakes actions.

Technology Stack

Foundation Models
AI Frameworks
Vector & Retrieval
Backend & APIs
Eval & MLOps
AI Development Tools
Cloud & Deployment

Our Work

Education🇺🇸 USA

Built an AI-powered digital learning platform for one of California's largest charter schools

Generative AI content recommendation engine for personalised student learning paths
Automated grading and assessment with LLM-based feedback, reducing teacher workload 50%
NLP-driven engagement tracking and at-risk student identification
RAG over curriculum documents so AI feedback stays grounded in school materials

97%

AI grading accuracy

50%

Faster turnaround

15K+

Students served

View Case Study
Highlands Brain AI learning platform
SaaS

AI-powered e-commerce intelligence platform with generative insights and content

LLM-powered listing optimisation — auto-generated product copy from structured data
Generative competitor analysis with weekly AI-written executive briefs
RAG-grounded chat assistant over a seller's full marketplace data
Sales forecasting models with explainable factors surfaced via LLM summaries

40%

Faster insights

25%

Revenue uplift

3×

Data processing speed

View Case Study
Ecomm Pulse AI intelligence platform
Travel & Hospitality

Integrated AI-powered smart pricing and automated guest communication for a vacation rentals platform

LLM-powered guest communication with multi-language support and tone control
Dynamic pricing engine combining ML forecasts with generative explanations for hosts
AI-generated property descriptions and amenity summaries from structured listing data
RAG-grounded support assistant with citations into the host knowledge base

50%

Booking efficiency

35%

Host productivity

99%

Platform uptime

View Case Study
StayVista AI smart pricing platform

Ready to Add Generative AI to Your Product? Let's Start With a Proof of Concept.

Talk to an Expert

Generative AI — Frequently Asked Questions

Generative AI development is the practice of building production systems on top of foundation models (GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.3) that can generate text, images, code, or audio. Traditional AI focused on prediction and classification; generative AI produces new content grounded in your data via retrieval-augmented generation (RAG), fine-tuning, and prompt engineering.

We work across closed and open models depending on cost, latency, residency, and capability requirements. Closed: OpenAI GPT-4o and o-series, Anthropic Claude 3.5 / 3.7, Google Gemini 1.5 / 2.5. Open: Meta Llama 3.3, Mistral, Qwen, DeepSeek. We pick per project — sometimes a multi-model router is the right answer.

Three layers: retrieval-augmented generation (RAG) so responses are grounded in your data, evaluation pipelines with golden test sets running on every change, and human-in-the-loop checkpoints for high-stakes decisions. We also instrument confidence scores and route uncertain outputs to human reviewers.

Yes. We fine-tune open-source models (Llama, Mistral, Qwen) on customer data for cost reduction, domain adaptation, or IP control. For closed models, we use OpenAI and Anthropic fine-tuning APIs where supported. Every project includes a clear data governance plan — your training data never leaves your environment without explicit consent.

Discovery and POC: 2–4 weeks. Production deployment with evals and monitoring: 8–12 weeks. Ongoing maintenance is a separate retainer covering model upgrades, prompt regression testing, and cost optimisation as token prices and capabilities shift.

Per-request token budgets, prompt compression, response caching, semantic deduplication, multi-model routing (cheap model for easy queries, premium model for hard ones), and continuous monitoring of cost-per-conversion. We make cost visible from day one so it never surprises.

Yes. We build production agents using LangGraph, OpenAI Agents SDK, Anthropic Claude tool use, and custom orchestration. Agents are scoped, sandboxed, and instrumented — with retries, fallbacks, and human-in-the-loop checkpoints for any irreversible action.

For regulated workloads we deploy to private endpoints (Azure OpenAI, AWS Bedrock, Vertex AI) or self-host open models on your infrastructure. Unico Connect is ISO 27001 and ISO 9001 certified. We map every project to GDPR, HIPAA, or sector-specific requirements as part of discovery.

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