From LLM fine-tuning to RAG pipelines and multimodal AI - we build production Gen AI systems that integrate with your product, respect your data, and deliver measurable ROI.
200+
Gen AI Features Shipped
30 days
Prototype to Prod
100%
Data Privacy Guaranteed
Inference Engine
Full Stack Integration
Core Stack
Tailwind Glassmorphism
"Build a complex React analysis dashboard."
"The quarterly report shows a 34% improvement..."
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What We Build
Production-ready Gen AI systems that integrate seamlessly into your existing workflows and products.
Train GPT-4, Llama, or Mistral on your proprietary data for domain-specific accuracy that generic models can't match.
Retrieval-Augmented Generation pipelines that ground AI answers in your live documents - no hallucinations, full citations.
Conversational AI for support, sales, HR, and ops. Multi-language, omni-channel, and integrated with your stack.
Automate product descriptions, marketing copy, reports, and personalised outreach at scale - with brand-consistent voice.
AI copilots that speed up your engineering team: autocomplete, test generation, PR review, and documentation.
Text-to-image, image understanding, video analysis, and audio transcription - across all media types in one unified pipeline.
Search that understands intent and meaning - not just keywords. Built on vector embeddings for precision at scale.
0+
Gen AI Features Shipped
0 days
Avg Prototype to Production
0%
Client Satisfaction
0×
Avg Productivity Gain
Industries We Build Gen AI For
Legal & LegalTechFinancial ServicesE-commerce & RetailHR & RecruitmentHealthcare & MedTechEdTech & TrainingPropTechMedia & PublishingSaaS PlatformsInsurance & InsurTechLogisticsEnergy & UtilitiesReal Applications
The Generative AI Opportunity
$1.3 trillion market by 2032. Early movers are locking in competitive advantages today.
Our Engineering Approach
Production-grade pipelines with safety layers, monitoring, and continuous improvement.
AI that reads, summarises, and flags risk clauses across thousands of contracts in minutes.
Industry ROI Benchmark
Generate optimised product descriptions at scale - personalised by audience, tone, and channel.
Industry ROI Benchmark
AI analyst that synthesises earnings data, news, and macro trends into narrative investor reports.
Industry ROI Benchmark
AI-assisted screening that reads CVs, scores candidates, and drafts personalised rejection and offer letters.
Industry ROI Benchmark
How We Build
We define the exact task, input/output format, quality bar, and guardrails. No ambiguity before a single token is generated.
Choose the right foundation model and architecture: closed API, open-source, fine-tuned, RAG-augmented - based on your latency, cost, and compliance needs.
Develop the pipeline, connect to your data sources and APIs, and implement safety layers - then test with real prompts against your quality benchmarks.
Production deployment with autoscaling, prompt logging, output monitoring, and continuous fine-tuning loops as new data arrives.
Legal SaaS
LegalEase
Fine-tuned Claude 3 on 180,000 commercial contracts. RAG pipeline grounds every output in the source document with full citation - zero hallucination tolerance enforced by structured output validation.
E-commerce / Retail
RetailCo Group
GPT-4o content pipeline generating audience-segmented, SEO-optimised product copy at scale. A/B tested against human-written copy across 6 categories - AI-generated outperformed by 28%.
Financial Services
TradePost Research
Multi-source RAG system ingesting earnings calls, SEC filings, and real-time news to produce structured analyst reports with source citations and per-claim confidence scores.
Stack chosen for your latency, compliance, and cost requirements - not default picks.
Foundation Models
RAG & Orchestration
Vector Databases
Fine-Tuning & Alignment
Image & Multimodal
Deployment & Serving
Foundation Models
RAG & Orchestration
Vector & Search
Fine-Tuning & Alignment
Image & Multimodal
Deployment & Serving
Safety & Guardrails
Questions
Traditional AI classifies, predicts, or makes decisions from existing patterns. Generative AI creates new content - text, images, code, audio - by learning the underlying distribution of training data. It's the difference between a sorting machine and a creative writer.
We implement strict data handling: no training data sent to external APIs unless explicitly scoped, on-premise options for sensitive workloads, PII redaction layers before any external call, and DPA agreements with all cloud providers. For regulated industries, we recommend self-hosted open-source models.
A well-scoped RAG chatbot or content generation tool typically ships in 4-8 weeks. Custom fine-tuning adds 2-4 weeks depending on data availability. We can often deliver a working prototype in the first two weeks.
We implement output validation, structured generation constraints, human-in-the-loop review for high-stakes outputs, and feedback loops that continuously improve quality. No AI system is 100% accurate - the goal is to make errors visible, caught early, and correctable.
Yes - that's our primary mode of working. We build Gen AI as features within existing products via API integration, not standalone tools. Your users interact with AI through your existing UX.
We implement a defence-in-depth model: input sanitisation and PII redaction before the prompt reaches the model, system prompt hardening, output validation against structured schemas, and real-time guardrails using tools like Guardrails AI, NeMo Guardrails, or LlamaGuard. For high-stakes applications we add a human-in-the-loop review layer for edge cases flagged by confidence scoring.
Yes - for clients with strict data residency or compliance requirements we deploy open-source models (Llama 3, Mistral, Phi-3) on your own infrastructure using vLLM, Ollama, or directly on AWS/Azure/GCP inside your VPC. Your data never leaves your environment. We've done this for clients in financial services, healthcare, and legal SaaS.
We treat hallucination as an engineering problem, not a model limitation. The primary tools: RAG grounding (every claim is traceable to a source document with a citation), structured output generation with JSON schema validation, self-consistency checks comparing multiple independent completions, and factual recall benchmarks tuned for your domain. We set up continuous evaluation pipelines using RAGAS or DeepEval so accuracy is measured - not assumed.
Tell us your use case and we'll show you how to build it in 30 days.