Generative AI & LLM Solutions

Build intelligent systems powered by large language models. From RAG systems to fine-tuned models, we architect generative AI that creates competitive advantage. Building successful tech business since 2013, 100+ products built, $1.5B+ in client revenue. We direct the AI - the AI does not direct us.

Generative AI LLM Solutions

80% of LLM Projects Fail in Production Because Teams Chase Capabilities, Not Problems

Generative AI capability has arrived. Every startup claims they use GPT-4. Every agency promises RAG systems. But capability without judgment is just expensive complexity. We build generative AI solutions with strategic thinking - identifying exactly where LLMs create business value, architecting systems that work reliably at scale, and preventing failure modes before they reach users.

RAG systems, fine-tuned models, prompt management, hallucination prevention - these are not exotic features anymore. They are standard practice at companies that make generative AI work. We have 12+ years of product experience and 100+ shipped systems. That foundation translates directly to LLM systems that deliver measurable business outcomes.

The competitive advantage is not in using LLMs. The advantage is in using them with judgment - knowing which problems they solve, which ones they do not, when they work reliably, and when you need a human in the loop. We bring that judgment to every project.

Why PixelForce for Generative AI?

We direct the AI. The AI does not direct us. Here is what 12+ years of product experience teaches about building generative AI that works.
  • Judgment beats hype. We understand which generative AI features create genuine competitive advantage versus which ones are technically impressive but do not move business metrics. Every feature must solve a real problem or we do not build it.
  • Production-ready from day one. Generative AI only matters when it works at scale with proper safeguards. We build hallucination prevention, confidence scoring, monitoring, and feedback loops from the start - not bolted on after.
  • Data-first thinking. LLM success depends on data quality. We assess data readiness, design collection pipelines, and understand which problems require fine-tuning versus RAG versus prompt engineering.
  • Technology-agnostic approach. We evaluate GPT-4, Claude, Llama, and specialist models based on your requirements - capability needed, cost constraints, latency, data privacy, custom training needs. We recommend the option that optimises your specific situation.
  • From strategy to scaling. We do not hand off after launch. We monitor model performance, adjust prompts and fine-tuning as patterns emerge, and help you continuously improve. Generative AI systems improve with usage - we build the infrastructure for that improvement.
  • Partners, not vendors. We embed into your team, challenge assumptions when necessary, and are transparent about what generative AI can and cannot do. We push back when we think a feature is not ready.

Our Generative AI & LLM Services

1. Generative AI Strategy & Discovery

Before you invest $200K+ in LLM development, know exactly where the value is. We spend 2-4 weeks in structured discovery: mapping business goals, understanding your data landscape, identifying highest-impact opportunities, assessing technology options, and building realistic cost and timeline estimates.

Deliverables: AI Opportunity Assessment, Technology Architecture Options, Data Readiness Audit, LLM Selection Framework, Cost and Timeline Projections.

2. Retrieval-Augmented Generation (RAG) Systems

Ground your LLM in proprietary knowledge. RAG solves hallucination, gives LLMs access to your knowledge bases and documents, and makes responses factual and reliable. We build RAG systems for customer support, knowledge management, document intelligence, and internal information retrieval.

Deliverables: Vector database setup and indexing, RAG pipeline architecture, Q&A interface, monitoring and evaluation framework, integration with your knowledge sources.

3. LLM Integration & API Optimisation

Integrate GPT-4, Claude, Llama, or other LLMs into your product. We handle API selection, prompt optimisation, cost management, latency reduction, and fallback strategies. We optimise your LLM costs through caching, batching, and right-sizing models.

Deliverables: Production-ready LLM integration, prompt management system, cost monitoring and optimisation, fallback and error handling, API documentation.

4. Custom Model Fine-Tuning

Specialise LLMs on your proprietary data. Fine-tuning improves performance on domain-specific tasks, reduces hallucinations, and can enable cheaper models to outperform larger base models. We handle data preparation (the hardest part), training, evaluation, and deployment.

Deliverables: Fine-tuned model optimised for your use case, training data pipeline, model evaluation framework, inference API, retraining and improvement strategy.

5. Prompt Engineering & Management

Crafting effective prompts is an art and science. We engineer prompts that extract consistent, high-quality outputs from LLMs. We build prompt management systems so you can version, test, and deploy prompts safely without redeploying code.

Deliverables: Optimised prompt library, A/B testing framework, prompt versioning and management system, documentation of prompt effectiveness.

6. Hallucination Prevention & Output Validation

Generative AI hallucinations are the biggest production risk. We implement confidence scoring, output validation, retrieval-augmented context, and human-in-the-loop workflows to prevent false outputs from reaching users.

Deliverables: Hallucination detection system, confidence scoring implementation, output validation rules, monitoring and alerting, feedback loop for continuous improvement.

7. Knowledge Base & Document Intelligence

Transform unstructured documents into searchable, queryable knowledge. We build systems that extract meaning from PDFs, documents, and knowledge bases, enabling semantic search and intelligent question-answering.

Deliverables: Document ingestion pipeline, semantic indexing, intelligent search interface, chat interface for knowledge queries, access controls and security.

8. AI App Scaling & Production Optimisation

Your proof-of-concept works with 100 requests per day. What about 10,000? We optimise LLM infrastructure for scale - latency reduction, cost optimisation, caching, load balancing, and reliability under volume.

Deliverables: Infrastructure optimisation, model serving setup, caching and performance improvements, cost analysis and optimisation, monitoring and alerting systems.

Generative AI for Different Business Types

SaaS Companies (Adding AI Features to Existing Products)

Your Challenge: You have customers and revenue. AI features can differentiate you from competitors, improve retention, and justify price increases. But AI features must feel polished and deliver obvious value, or they backfire.

Our Approach: Strategic feature selection using discovery, focused implementation on highest-impact features, exhaustive testing before release. We launch features that excite customers and actually work.

Typical Investment: $120K-$300K per AI feature set | Timeline: 4-8 months

Enterprise & Corporate (Automating Knowledge Work)

Your Challenge: Teams spend time on repetitive, knowledge- intensive work - analysing documents, extracting data, writing summaries, research. LLMs can automate significant portions of this work.

Our Approach: Identify highest-impact workflows, build intelligent automation that augments humans (not replaces), integrate with existing systems. Reduce manual work while maintaining quality control.

Typical Investment: $150K-$400K | Timeline: 6-9 months from discovery to production

Startups Building AI-First Products

Your Challenge: Building a product where generative AI is central to your value proposition. You need to validate that customers want AI features, that they work reliably, and that you can acquire users cost-effectively.

Our Approach: Lean generative AI MVP that proves market value without over-engineering. Focus on user problem first, AI complexity second. Test assumptions with real users and iterate rapidly.

Typical Investment: $80K-$200K | Timeline: 4-6 months to market-ready product

Content & Publishing (Content Generation & Optimisation)

Your Challenge: Scaling content creation - whether blog posts, marketing copy, product descriptions, or personalised recommendations.

Our Approach: Fine-tune models on your content style and brand voice. Build content generation pipelines with human review and editing workflows. Combine LLM output with your editorial standards.

Typical Investment: $100K-$250K | Timeline: 3-5 months

Customer Support & Service (Intelligent Support Systems)

Your Challenge: Support teams are overwhelmed. Too many repetitive questions, not enough time for complex issues requiring judgment.

Our Approach: Build RAG-powered chatbot that answers common questions using your actual knowledge base, routes complex issues to humans, learns from support interactions over time.

Typical Investment: $80K-$200K | Timeline: 4-8 weeks for basic system, 4-5 months for advanced features

Healthcare & Regulated Industries (Compliance-First Generative AI)

Your Challenge: Generative AI in healthcare, financial services, and legal requires explainability, audit trails, and human oversight from day one - not retrofitted.

Our Approach: Build generative AI as decision support, not autonomous replacement. Ensure explainability of outputs, maintain audit trails, implement human-in-the-loop workflows, design for regulatory compliance from the start.

Typical Investment: $200K-$500K+ | Timeline: 8-12 months including compliance validation

Generative AI & LLM Development Pricing

Transparent pricing based on scope, complexity, and the extent of custom model work:

  • Generative AI Discovery & Strategy: $15K-$25K - Structured 2-4 week assessment identifying where generative AI creates competitive advantage, technology architecture recommendations, data readiness audit, and realistic cost and timeline estimates.
  • LLM Integration / RAG System: $80K-$180K - Implement Retrieval-Augmented Generation or direct LLM integration (GPT-4, Claude, Llama). Includes vector database setup, prompt optimisation, and monitoring. Timeline: 12-20 weeks.
  • Custom Fine-Tuned Solution: $150K-$350K - Fine-tune LLMs on proprietary data, custom model development, advanced prompt management and evaluation. Significant time for data preparation and iterative refinement. Timeline: 16-24 weeks.
  • Enterprise Generative AI Platform: $350K-$600K+ - Large- scale systems with multiple models, sophisticated data pipelines, model orchestration, extensive monitoring and observability. Timeline: 6-12 months+.

What influences final cost: Data quality and readiness (data preparation can be 40-50% of work), custom model training requirements, API costs (varies with usage volume), infrastructure complexity, compliance and security requirements (healthcare, regulated industries cost more).

Payment Structure: Milestone-based payments aligned to development phases. Typically 30% at project start, 40% at development milestones, 30% at launch. Fixed scope, transparent pricing.

Additional Budget Lines: LLM API costs (we manage and optimise), data labelling services (if outsourced), infrastructure (AWS, GCP, or on-premise compute). We quantify all of these during discovery.

Frequently Asked Questions of Generative AI & LLM Solutions

Generative AI systems like large language models (LLMs) can produce new content - text, code, images, data - based on patterns learned from training data. Unlike traditional AI which classifies or predicts existing categories, generative AI creates novel outputs.

Business applications: Automating content creation and summarisation, intelligent customer support through conversational AI, knowledge extraction from unstructured documents, code generation and technical documentation, predictive analytics and decision support, and personalised recommendations at scale.

The key question is not "Can we use LLMs?" but "Where do LLMs create competitive advantage specific to our business?" That is where we focus during discovery. We have built 100+ products generating $1.5B+ in client revenue - we know the difference between AI features that matter and features that sound impressive but do not move business metrics.

Generative AI development pricing depends on complexity, data integration, and whether you need custom models:

Generative AI Discovery & Strategy: $15K-$25K - Assessment of AI opportunities, technology selection, data requirements, and realistic cost estimates. Clear output: where AI creates value and honest investment projections.

LLM Integration / RAG System: $80K-$180K - Integrate existing LLMs (GPT-4, Claude, Llama) with Retrieval-Augmented Generation for knowledge base queries, document intelligence, or customer interactions. Timeline: 12-20 weeks.

Custom Fine-Tuned Solution: $150K-$350K - Fine-tune LLMs on proprietary data, custom model development, advanced prompt management. Timeline: 16-24 weeks.

Enterprise Generative AI Platform: $350K-$600K+ - Large- scale systems with sophisticated data pipelines, model orchestration, extensive monitoring. Timeline: 6-12 months+.

We provide transparent pricing after discovery. Our Scoping & Design phase reduces scope creep and unnecessary complexity.

RAG solves a critical problem with large language models: they do not have access to your proprietary data and they hallucinate (make up plausible but false information).

Traditional LLM approach: "Write a customer support response to this question." The model generates a response, but it cannot reference your product documentation, company policies, or customer history. The response might sound good but be factually wrong.

RAG approach: Before asking the LLM to respond, first retrieve relevant documents from your knowledge base using vector embeddings. Feed those documents to the LLM as context. Now the LLM responds based on your actual knowledge, not from training data. The output is grounded in your reality.

Why it matters: RAG systems are the most practical way to give LLMs access to proprietary knowledge without retraining models. They work reliably in production and significantly reduce hallucinations. If you want to build an intelligent knowledge base, customer support system, or document intelligence product, RAG is the foundation.

RAG markets are projected to reach $9.86B by 2030. It is no longer experimental - it is how enterprise LLM systems actually work.

Prompt Engineering - Crafting the input to an LLM to get better outputs. Think of it as instruction-tuning without any training. Cheap, fast, often effective.

When prompt engineering works: You need GPT-4 or Claude to follow specific instructions better. You want A/B testing different prompts. You need fast iteration. You do not have domain-specific data.

Fine-tuning - Training a model on your proprietary data to specialise its behaviour. The model learns task-specific patterns from examples you provide.

When fine-tuning matters: You have hundreds or thousands of labelled examples showing the exact output format and style you need. Your domain is niche or technical (legal language, medical terminology, code generation). You want to reduce hallucinations by teaching the model domain-specific knowledge. You need cost optimisation by using smaller, cheaper models.

Our approach: Start with prompt engineering. Measure results. If results are good, ship. If results are inconsistent or your examples show consistent patterns the base model misses, move to fine-tuning. Do not fine-tune speculatively. Do not prompt-engineer when fine-tuning would solve your problem more reliably.

Hallucination - when an AI confidently generates false information - is the single biggest risk with generative AI in production. You cannot eliminate it completely, but you can control it.

Strategy 1: RAG Systems - Do not ask the LLM to know something. Retrieve the information from your knowledge base first, pass it to the LLM as context. If the information is not in your knowledge base, the LLM says "I do not have that information" rather than making something up.

Strategy 2: Confidence Scoring & Thresholds - Not all LLM outputs should be trusted equally. We implement confidence scoring. If confidence is below threshold, escalate to a human or return a default response instead of trusting the potentially hallucinated answer.

Strategy 3: Output Validation - For structured outputs, we parse and validate against business rules before returning to users. If output violates constraints, we log it and escalate or try again.

Strategy 4: Monitoring & Feedback - We track when users reject or correct LLM outputs. That feedback identifies hallucinations in the wild so we can adjust prompts, add constraints, or improve training data.

Strategy 5: Human-in-the-Loop - For high-stakes decisions (medical, legal, financial), we do not let AI decide. AI makes a recommendation, a human approves. This is not a technical failure - it is smart architecture.

The companies doing LLM right in production are not trying to eliminate hallucination. They are designing systems where hallucinations cannot cause damage.

There is no one answer - it depends on your requirements. We stay technology-agnostic because the right choice depends on the problem.

OpenAI GPT: Most capable, best at general-purpose tasks. Excellent for code generation, analysis, creative writing. Expensive. Closed-source. Mature ecosystem. Good choice if capability is your top priority.

Anthropic Claude: Strong reasoning and analysis. Excellent for handling long documents. Lower hallucination rate in testing. Good API documentation. Great for knowledge work and RAG systems. Mid-range cost.

Open-source (Llama 2, Mistral, Falcon): Can be self-hosted. Lower cost at scale. Less capable than OpenAI GPT or Claude. Good for: keeping data private, custom fine-tuning, cost-sensitive applications, or building proprietary models.

Smaller specialist models: Sometimes a 7B or 13B parameter model fine-tuned on your data outperforms OpenAI GPT. Cheaper to run. Worth evaluating if latency or cost is critical.

Our approach during discovery: We assess your requirements (capability needed, latency requirements, cost constraints, data privacy, custom training needs) and recommend the model that optimises your specific situation. We often recommend starting with OpenAI GPT or Claude API while you validate the concept. If volume or privacy becomes an issue, we evaluate open-source alternatives.

Yes - but it depends on which LLM and how you use it.

Using public APIs (OpenAI, Anthropic): Your data is sent to their servers. They have privacy policies, but data leaves your control. For some businesses and some use cases, this is acceptable. For regulated industries or sensitive data, it is not.

Enterprise agreement options: Both OpenAI and Anthropic offer enterprise agreements with data privacy guarantees. Your data is not used for model training. We can help you negotiate these.

Self-hosted open-source models: Deploy Llama, Mistral, or other open-source models on your own infrastructure. Your data never leaves your systems. Trade-off: you manage infrastructure and model updates. Cost is higher at large scale than using APIs.

Hybrid approach (common in practice): Self-host models for sensitive data processing. Use APIs for non-sensitive tasks. This balances cost, capability, and privacy.

During discovery, we assess: What data needs to stay private? What is the risk if it reaches a third party? What is your infrastructure appetite? What is your budget? Then we recommend the approach that balances all constraints.

Privacy is important. It is also not an absolute requirement for every use case. We help you think through the tradeoffs.

Generative AI Discovery & Strategy: 2-4 weeks. Assessment and planning only, no development.

LLM Integration / RAG MVP: 12-16 weeks. Validate that RAG works for your use case using existing LLMs and vector databases. Focused scope.

Production LLM System: 16-24 weeks. Full development including API integration, UI/UX, monitoring dashboards, evaluation framework, deployment.

Custom Fine-Tuned Solution: 20-32 weeks. Add significant time for data collection, labelling, model training, and iteration.

Enterprise Platform: 6-12 months+. Multiple model orchestration, large data pipelines, compliance requirements, significant integration work.

What changes timelines most: Data quality and readiness. If your data is clean, labelled, and ready, timelines compress. If data is scattered across legacy systems or unlabelled, data preparation becomes the critical path. We assess data readiness during discovery and build realistic timelines.

Parallelisation matters. While engineers build the system, we can start fine-tuning data collection. We keep the critical path moving.

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