Our Generative AI Development Services
From custom LLM applications and RAG pipelines to AI copilots and fine-tuned domain models, every engagement is staffed with engineers who have shipped generative AI systems in production, not just run experiments.
We build production-ready applications powered by large language models: AI-powered search, document Q&A, writing assistants, summarization tools, and knowledge management systems tailored to your business domain. Each system ships with monitoring and guardrails, not just a working demo.
- Domain-specific document Q&A and search
- AI writing assistants with brand voice controls
- Knowledge management and enterprise wikis
- Summarization pipelines for reports and research
We fine-tune foundation models (GPT-4, Claude, LLaMA 3, Mistral) on your proprietary data for domain-specific accuracy. If your use case demands precise industry terminology, company-specific workflows, or knowledge the base model simply doesn't have, fine-tuning is the answer.
- Fine-tuning on GPT-4, Claude, LLaMA 3, Mistral
- Domain-specific accuracy improvements
- Instruction tuning and RLHF alignment
- Full data privacy: your data stays yours
Retrieval-Augmented Generation keeps your LLM grounded in facts. We design and build RAG systems that pull from your actual knowledge base at query time, dramatically reducing hallucinations and keeping answers current. We use vector databases (Pinecone, Weaviate, pgvector) and semantic chunking strategies tuned per use case.
- Vector database setup (Pinecone, Weaviate, pgvector)
- Semantic chunking and embedding pipelines
- Hybrid search (dense + sparse retrieval)
- Hallucination rate reduction and eval frameworks
We build intelligent copilots that work inside your existing tools: contextual suggestions, knowledge retrieval, task automation, and decision support without breaking your team's workflow. These aren't generic chatbots. They know your systems, your data, and your processes.
- In-app copilots for SaaS and enterprise tools
- Contextual task suggestions and auto-completion
- Enterprise knowledge retrieval and Q&A
- Integration with Slack, Teams, Notion, and CRMs
You don't need to rebuild everything. We embed generative AI into your existing CRMs, ERPs, knowledge bases, and business applications via APIs and middleware. Your current systems get smarter without a full rewrite.
- OpenAI, Anthropic, Cohere, and Gemini API integration
- Middleware design for existing application stacks
- CRM and ERP AI enhancement (Salesforce, SAP)
- Streaming response and async job handling
Not every AI problem needs a custom model. We run AI readiness assessments, map high-value use cases to your business goals, and give you a clear implementation roadmap: build vs. fine-tune vs. API, with honest ROI analysis so you invest where it actually pays off.
- AI readiness and data maturity assessment
- Use case prioritisation with ROI modelling
- Build vs. fine-tune vs. API decision framework
- Implementation roadmap and team capability planning
Ready to Put Generative AI to Work in Your Business?
From proof-of-concept to production, we build GenAI systems that deliver real outcomes, not just demos. Fully signed NDA. No obligation.
Schedule a Free GenAI ConsultationFoundation Models We Work With
We're model-agnostic. We select the right foundation for your use case, whether API-based or self-hosted.
GPT-4 / GPT-4o
APIBest for advanced reasoning, multi-modal understanding, and general-purpose LLM applications requiring top-tier accuracy.
Claude (Anthropic)
APIIdeal for long-context document processing, nuanced analysis, and enterprise use cases where safety and reliability matter.
LLaMA (Meta)
Open SourceOpen-source foundation model for self-hosted deployments where full data privacy and zero third-party API access are required.
Mistral
Open SourceHigh-performance open-source model with strong cost-efficiency, our preferred option for high-throughput production workloads.
Gemini (Google)
Multi-modalMulti-modal capabilities with deep Google Cloud integration, strong for vision, video, and Google Workspace-connected applications.
Cohere
EnterpriseEnterprise-focused embeddings, semantic search, and text generation, built for search and retrieval-heavy enterprise pipelines.
DALL-E / Stable Diffusion
Multi-modalImage generation, visual content creation, and design automation for marketing teams and product design pipelines.
Whisper (OpenAI)
AudioBest-in-class speech-to-text for audio transcription, voice-driven interfaces, and meeting intelligence applications.
Custom / Open-Source Models
CustomDomain-specific models trained from scratch or adapted from open-source checkpoints for maximum accuracy on proprietary datasets.
We don't recommend models based on partnerships or preferred vendors. GPT-4o, Claude, LLaMA, Mistral, and Gemini each have different strengths. We run structured evaluations against your specific requirements (accuracy, latency, cost, and data privacy) and recommend accordingly. The right model is the one that performs best for your use case, not the most popular one.
Generative AI in Practice
These are the specific applications we build and deploy in production, mapped to real business outcomes, not AI buzzwords.
Automate extraction, classification, summarisation, and Q&A from contracts, invoices, legal documents, and research reports. We build pipelines that handle unstructured text at scale, cutting manual review time by 70–90%.
Marketing copy, product descriptions, email campaigns, social content, and blog drafts, generated on-demand with your brand voice baked in. The output is consistent, on-brief, and production-ready without a full editorial pass.
Enterprise knowledge management where employees ask questions in plain language and get answers grounded in your actual documentation, policies, and internal data, not hallucinated summaries.
AI-assisted coding, automated code review, documentation generation, test case creation, and technical debt analysis. We build developer copilots and internal tools that integrate directly into your existing CI/CD workflow.
Intelligent chat and voice agents that handle complex queries, pull from live knowledge bases, and escalate to human agents with full context when needed. Resolve 60–80% of support queries without human intervention.
AI-driven product, content, and service recommendations that adapt to user behaviour, preferences, and real-time signals. Goes beyond collaborative filtering to deliver contextually relevant suggestions.
Natural language querying of databases and automated report generation from raw data. Business teams ask questions in English and get structured answers, no SQL required.
AI-generated visuals, design variations, mockups, and brand assets for marketing and product teams. Built with DALL-E and Stable Diffusion, integrated into your existing creative tools and approval workflows.
Multi-language content generation and real-time translation with context-aware accuracy. Handles technical terminology, brand tone, and regional nuance that standard translation APIs miss.
Generative AI Solutions We've Delivered
Real systems. Real outcomes. Each case study below covers the full lifecycle, from data assessment through production deployment.
A UK legal technology company needed to reduce the time lawyers spent reviewing contracts, a process that averaged 4 hours per contract. Manual review was a bottleneck blocking growth.
We built a RAG-powered contract analysis system using GPT-4 and a Pinecone vector database, pre-loaded with 50,000+ legal precedents. The system extracts clauses, flags non-standard terms, and generates a review summary in under 90 seconds.
A US SaaS platform with 1.2 million users needed an in-app AI assistant that could answer product questions, guide onboarding, and reduce support ticket volume, without hallucinating product information.
We built a fine-tuned Claude-based copilot, trained on their full documentation corpus and support ticket history. Integrated directly into the product UI via a streaming API, with a confidence threshold that surfaces human agents for low-certainty queries.
A European health data company needed to generate structured clinical summaries from unstructured physician notes, fast enough for real-time use in patient consultations, and accurate enough to meet HIPAA-equivalent EU regulatory requirements.
We fine-tuned a LLaMA 3 model on de-identified clinical notes using LoRA adapters, deployed fully on-premises to meet data residency requirements. A structured output schema ensures consistent formatting across all generated summaries.
Generative AI Across Industries
Generative AI use cases differ significantly by industry. Here's where we've seen the clearest ROI.
- Clinical documentation and note summarisation
- Medical report generation and patient communication
- Drug research and literature review automation
- Financial report generation and analysis
- Regulatory document review and compliance checking
- Fraud narrative detection and investigation support
- Product description generation at scale
- Personalised shopping assistants and recommendations
- Customer query handling and returns automation
- Contract analysis and clause extraction
- Legal research automation and case summarisation
- Document drafting and precedent retrieval
- AI tutors and personalised learning pathways
- Curriculum generation and adaptive content
- Automated grading and feedback generation
- Technical documentation and maintenance manual generation
- Quality report generation from sensor data
- Predictive maintenance narrative summaries
- Claims summarisation and damage report generation
- Policy document generation and comparison
- Underwriting analysis and risk narrative creation
- Content creation: scripts, articles, and ad copy
- Personalised content recommendations at scale
- Automated subtitles, translations, and localisation
- Shipment document processing and customs paperwork automation
- AI-generated tracking updates and customer notifications
- Route and inventory report summarisation from operational data
Our Generative AI Development Process
From initial use case discovery through to production monitoring. No black boxes. You know exactly what's happening at each stage.
We start with your business goals, not the technology. Working with your team, we identify the highest-value generative AI use cases, assess your data readiness, and define measurable success criteria. We also make the build-vs-fine-tune-vs-API call here, so you're not locked into the wrong architecture six months in.
Most AI projects fail on data, not models. We collect, clean, deduplicate, and structure your proprietary data for fine-tuning or RAG pipeline ingestion. We address quality gaps, bias risks, and compliance requirements upfront, before a single model trains.
We select the right foundation model based on your specific use case, data privacy requirements, cost constraints, and latency targets, not vendor preference. The architecture document covers the full system: API layer, model serving, vector store, caching, and fallback behaviour.
We build the application layer, fine-tune models on your proprietary data, implement RAG pipelines, configure vector databases, and develop prompt engineering frameworks with chain-of-thought and structured output patterns. Everything gets version-controlled and tested before it reaches staging.
LLM output quality requires a different evaluation approach. We measure accuracy, hallucination rate, latency, and edge case behaviour using automated eval frameworks (LangSmith, Braintrust) and human-in-the-loop review. We A/B test prompt strategies and document failure modes before launch.
We deploy to your target environment (AWS, Azure, GCP, or on-premises) and set up production monitoring for model performance, cost-per-query tracking, and output quality drift. You get live dashboards, alerting, and a support SLA that covers retraining cycles, not just infrastructure incidents.
Why Choose Avenotech for Generative AI Development
There's no shortage of teams who can demo a ChatGPT wrapper. Here's what separates production GenAI engineering from a weekend project.
We've seen too many GenAI projects die between the proof-of-concept and production. We engineer systems with monitoring, guardrails, fallback logic, and reliability built in from the start. Every system we ship handles real workloads.
GPT-4, Claude, LLaMA 3, Mistral: we work with all of them. We recommend the right model for your use case, privacy requirements, and budget. No vendor lock-in, no hidden reseller margins on API costs.
Your data stays yours. We build on-premises, private cloud, or API-based solutions with encryption at rest and in transit, role-based access controls, and compliance coverage for GDPR, HIPAA, and SOC 2.
We don't just call APIs. We build production RAG pipelines, fine-tune models on proprietary datasets, and engineer systems for accuracy, retrieval precision, and low hallucination rates, backed by measurable eval metrics.
GenAI that fits your existing stack, not a standalone tool nobody uses. We integrate with your CRM, ERP, knowledge base, ticketing system, and internal workflows via APIs, webhooks, and custom middleware.
Clear success metrics agreed upfront. Sprint-based delivery with regular demos. Full knowledge transfer: your team understands what we built and why. You own every line of code, every model weight, every data pipeline.
Frequently Asked Questions About Generative AI Development
Answers to the questions we hear most often from CTOs and product teams evaluating a generative AI engagement.
What is generative AI and how can it help my business?
Generative AI refers to models that create new content (text, images, code, and data) rather than just classifying or predicting. In practice, this means you can automate content production, extract structured information from documents, build customer-facing chat tools that actually understand context, assist developers with code generation, and surface knowledge buried in your internal systems. The business impact varies by use case, but the highest-ROI applications we see consistently are document processing, customer support automation, and internal knowledge management.
How much does generative AI development cost?
The cost depends on several factors: complexity of the use case, whether you need custom fine-tuning or a RAG pipeline versus a simpler API integration, the volume and quality of your existing data, and the level of system integration required. A proof-of-concept using existing APIs typically takes 4–8 weeks. A production-grade RAG system or fine-tuned model deployment is a more substantial engagement. We scope each project individually and provide a detailed breakdown before any work begins, no surprises.
How long does it take to build a generative AI solution?
A proof-of-concept typically takes 4–6 weeks. A production-ready solution, with fine-tuning, evaluation, integration, monitoring, and deployment, ranges from 2 to 6 months depending on data readiness, integration complexity, and the number of use cases in scope. Data preparation is usually the longest phase, particularly if your internal data needs significant cleaning or labelling before it can be used for fine-tuning.
What's the difference between fine-tuning and RAG?
Fine-tuning means training a foundation model on your own data so it learns domain-specific knowledge, terminology, and behaviour permanently. The result is a model that genuinely understands your domain, but training takes time, costs more upfront, and needs to be repeated when your data changes significantly. RAG (Retrieval-Augmented Generation) retrieves relevant documents from your knowledge base at query time and passes them to the model as context. It's faster to set up, cheaper to maintain, and handles frequently changing data well. The right choice depends on your use case: fine-tuning for specialised behaviour and terminology, RAG for grounding responses in current, factual data.
Which foundation model should we use?
The right choice depends on your specific requirements. GPT-4o performs best on general reasoning and instruction-following tasks. Claude 3.5 handles long-context documents particularly well and is strong for analysis-heavy applications. LLaMA 3 and Mistral are your go-to options for self-hosted deployments where data privacy mandates that nothing leaves your infrastructure. Cohere is strongest for semantic search and enterprise retrieval use cases. We run a structured model selection process at the start of every engagement, comparing options against your specific performance, cost, and compliance requirements before recommending anything.
Can generative AI be integrated with our existing systems?
Yes. We integrate generative AI into CRMs (Salesforce, HubSpot), ERPs (SAP, Oracle), knowledge bases (Confluence, Notion, SharePoint), customer support platforms (Zendesk, Intercom), and custom applications via REST APIs, webhooks, and middleware. The integration approach depends on your existing architecture. We design it to fit your stack, not the other way around.
How do you prevent AI hallucinations?
Hallucinations are a real problem, and no model is immune. We manage them through multiple layers: RAG grounding that ties responses to your actual data, structured output schemas that constrain what the model can generate, confidence thresholds that route uncertain queries to human agents, output validation against known facts, and ongoing monitoring for quality drift in production. Our evaluation frameworks measure hallucination rates before and after deployment and set measurable baselines the system must maintain.
Is our data safe when using generative AI?
It depends on your deployment model, and we help you choose the right one. For organisations with strict data residency or privacy requirements (healthcare, legal, financial services), we deploy fully on-premises or in a private cloud environment using open-source models like LLaMA or Mistral, so no data ever reaches a third-party API. For teams comfortable with API-based deployment, we use OpenAI's and Anthropic's enterprise tiers, which include data processing agreements and explicit no-training-on-your-data commitments. Everything is encrypted in transit and at rest.
Do you provide ongoing support after deployment?
Yes. Every production deployment includes a support SLA covering model monitoring, performance optimisation, retraining when data or requirements change, cost optimisation, and feature updates. LLM systems behave differently at scale than in testing. Production monitoring is not optional, and we treat it as part of the engagement, not an afterthought.
Ready to Build with Generative AI?
From LLM-powered applications to enterprise RAG pipelines, let's build AI that delivers real business impact. Tell us about your use case and we'll come back with a clear plan.