Build GenAI That Goes Beyond the Demo. Into Production.
From enterprise RAG pipelines and LLM fine‑tuning to autonomous AI agents and multimodal systems — SourceMash engineers Generative AI solutions that are accurate, reliable, secure, and genuinely useful at scale inside your organisation. No hallucination‑prone prototypes. No vendor lock‑in. Real production AI.
OUR GENAI PRACTICES
Six Deep Practices. One Production-Ready GenAI Partner.
Generative AI is not a single technology — it is a family of capabilities that must be architected, engineered, evaluated, and governed carefully to deliver enterprise-grade reliability. Our six practices cover the full GenAI stack.
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LLM Integration & API Development
The most powerful AI models in the world are now available via API — but connecting them to real enterprise systems requires deep engineering that goes far beyond a few lines of SDK code. SourceMash designs and builds production‑grade integration layers between foundation models (GPT‑4o, Claude, Gemini, Llama 3, Mistral) and your enterprise applications — with prompt engineering, output validation, cost management, latency optimisation, fallback logic, and the full observability stack your production systems demand.
Common Enterprise LLM Integration Use Cases
Customer Support Automation
LLM‑powered support agents resolving tier‑1 tickets without human escalation
Document Summarisation
Automated summarisation of contracts, reports, and research at scale
Data Extraction & Structuring
Converting unstructured text into validated, structured JSON outputs
Intelligent Content Generation
On‑brand product descriptions, emails, and reports generated at scale
Multi‑LLM API Integration & Routing
Intelligent LLM routing architectures that select the optimal model for each request — routing simple tasks to cost‑efficient models (GPT‑4o‑mini, Claude Haiku, Llama 3) and complex reasoning to frontier models (GPT‑4o, Claude Sonnet/Opus) — reducing API costs by up to 60% while maintaining output quality, with automatic failover between providers to eliminate single‑provider dependency.
Structured Output & Function Calling
Reliable LLM integrations that produce validated, typed outputs your applications can depend on — using OpenAI structured outputs, Anthropic tool use, JSON schema validation, and Pydantic models to constrain LLM responses to well‑defined formats, enabling safe integration into downstream business logic, databases, and APIs without brittle string parsing.
Prompt Engineering & Optimisation
Systematic prompt design, testing, and optimisation — applying chain‑of‑thought prompting, few‑shot learning, meta‑prompting, and automated prompt optimisation (DSPy, PromptFoo) to maximise output quality, consistency, and task accuracy for your specific enterprise use cases, with version control and A/B testing frameworks for ongoing prompt improvement.
Streaming & Real‑Time LLM APIs
Production streaming LLM API implementations with server‑sent events, WebSocket streaming, and progressive response rendering that eliminate the perceived latency of waiting for full LLM completions. We also implement intelligent semantic similarity caching with GPTCache and request batching to dramatically reduce API costs at high request volumes.
LLM Observability & Evaluation
Full observability stack for production LLM applications — capturing every prompt, completion, latency, token count, cost, and user feedback signal; running automated evaluation pipelines that score output quality using LLM‑as‑judge and RAGAS metrics; and providing dashboards that give your AI product team the insight to continuously improve model performance.
Private & On‑Premise LLM Deployment
Deploy open‑source LLMs entirely within your own infrastructure — AWS, Azure, GCP, or on‑premise GPU servers — using vLLM, Ollama, and Triton Inference Server, ensuring sensitive enterprise data never leaves your security perimeter and achieving GDPR, HIPAA, and SOC 2 compliance without sacrificing the quality of your generative AI applications.
LLM Integration Core Capabilities
Enterprise System Integration
Connect LLMs to Salesforce, SAP, ServiceNow, SharePoint, and internal APIs for seamless AI‑powered workflow automation.
Token & Cost Optimisation
Token compression, context window management, caching strategies, and model routing to control LLM spend at scale.
PII Redaction & Data Security
Pre‑processing pipelines that detect and redact sensitive data before it reaches any external LLM API endpoint.
Fallback & Resilience
Multi‑provider fallback chains, circuit breakers, and graceful degradation ensuring 99.9% availability for LLM‑powered features.
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RAG Systems & Enterprise Knowledge Bases
Retrieval‑Augmented Generation is the most impactful GenAI pattern for enterprises with proprietary knowledge — and building a RAG system that works reliably in production is far harder than the demos suggest. SourceMash designs and engineers production RAG architectures that ground every LLM response in your authoritative enterprise knowledge: documentation, policies, product catalogues, contracts, research, and real‑time data. We go beyond naïve RAG to advanced retrieval strategies, hybrid search, reranking, and hallucination mitigation that your users can actually trust.
RAG Architecture Design & Implementation
End‑to‑end RAG system design — from document ingestion, chunking strategy selection (fixed, recursive, semantic, and agentic chunking), embedding model selection, and vector store configuration, to retrieval pipeline design with metadata filtering, query expansion, and context assembly that maximises the relevance and factual accuracy of LLM‑generated answers.
Hybrid Search & Advanced Retrieval
Move beyond naive dense vector search to production hybrid retrieval — combining dense semantic search with sparse BM25 keyword search using reciprocal rank fusion, query decomposition for multi‑hop retrieval, HyDE (Hypothetical Document Embeddings) for improved recall, and cross‑encoder reranking to ensure the most relevant context reaches your LLM every time.
Vector Database Selection & Optimisation
Architecture and implementation of enterprise vector databases — evaluating and configuring Pinecone, Weaviate, Qdrant, Chroma, pgvector, and Milvus for your specific scale, latency, and cost requirements, with index strategies, namespace partitioning, and filtering architectures that support multi‑tenant RAG systems serving thousands of users across different knowledge domains.
Multi‑Modal Document Ingestion Pipelines
Robust document processing pipelines that ingest, parse, and index enterprise content at scale — handling PDFs, Word documents, PowerPoint presentations, spreadsheets, HTML, images with OCR text extraction, and structured database content, with table extraction, layout‑aware parsing, and incremental update pipelines that keep your knowledge base current without full re‑indexing.
RAG Evaluation & Quality Optimisation
Systematic RAG evaluation and continuous improvement — establishing ground truth question‑answer datasets for your domain, measuring retrieval recall, context precision, answer faithfulness, and answer relevance using RAGAS, and building automated regression testing suites that catch retrieval quality regressions before they reach production, with A/B tests on chunking and retrieval strategies.
GraphRAG & Knowledge Graph Integration
Advanced RAG architectures that leverage knowledge graphs alongside vector retrieval — using Microsoft GraphRAG or custom Neo4j‑based implementations to capture entity relationships and multi‑hop reasoning capabilities that flat vector search cannot provide, enabling complex relational queries that require understanding of how concepts in your enterprise knowledge base connect to one another.
RAG Core Capabilities
Multi‑Tenant RAG
Namespace‑isolated architectures serving multiple business units from a single platform with strict data segregation.
Real‑Time Knowledge Updates
Incremental indexing pipelines that keep your knowledge base current as source documents change — no full re‑indexing required.
Citation & Source Attribution
Every RAG response includes verifiable source citations with document references and passage highlights that users can verify.
Multilingual RAG
RAG systems that retrieve and answer in 30+ languages using multilingual embedding models and cross‑lingual retrieval.
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AI Agents & Agentic Workflows
AI agents represent the next frontier of enterprise automation — systems that don’t just answer questions but plan, reason, use tools, take actions, and complete complex multi‑step business tasks with minimal human oversight. SourceMash designs and engineers autonomous AI agent systems for enterprise use cases: research automation, code generation, data analysis, workflow orchestration, and multi‑agent collaboration. We build agents that are capable enough to be genuinely useful and constrained enough to be genuinely safe.
Single‑Agent System Design
Focused single‑agent systems for well‑defined enterprise tasks — research agents that browse, synthesise, and report; data analysis agents that write and execute code against your databases; document processing agents that extract, validate, and route structured information; and customer‑facing agents that resolve queries by accessing live business systems and knowledge bases autonomously.
Multi‑Agent Orchestration Systems
Complex multi‑agent architectures where specialised agents collaborate to complete tasks beyond any single agent’s capability — using supervisor/worker patterns, peer‑to‑peer agent communication, and shared memory systems to coordinate research agents, writing agents, code agents, and validation agents in workflows that mirror the collaborative intelligence of expert human teams.
Custom Tool & Function Development
Build the tool ecosystem your agents need to act in the real world — custom function definitions connecting agents to your internal APIs, databases, ERP systems, CRM data, file systems, code execution environments, and external services. Every agent tool call is logged and auditable for safety and compliance purposes.
Agent Memory & State Management
Sophisticated agent memory architectures — short‑term working memory, long‑term episodic memory, semantic memory, and procedural memory using vector databases, structured stores, and knowledge graphs — giving your agents the ability to learn from past interactions and maintain context across long‑running workflows that span hours or days.
Workflow Automation with AI Agents
Replace brittle RPA with intelligent AI agents that handle variability, exceptions, and ambiguity — automating document processing, approval routing, data reconciliation, report generation, and compliance checking with agents that understand business context rather than rigid scripts, and escalate to humans when genuinely uncertain.
Agent Evaluation & Reliability Engineering
Systematic evaluation frameworks for agent reliability — measuring task completion rates, tool call accuracy, reasoning faithfulness, goal achievement, and safety compliance across hundreds of test scenarios before any agent touches production. We implement agent tracing, step‑level logging, failure mode analysis, and automated regression testing to maintain reliability as you update models and tools.
AI Agent Core Capabilities
Human‑in‑the‑Loop Design
Thoughtful human oversight checkpoints at high‑stakes decision points — agents that know when to escalate and when to act autonomously.
Full Agent Auditability
Complete logging of every agent reasoning step, tool call, and decision for compliance, debugging, and continuous improvement.
Long‑Running Agent Tasks
Asynchronous agent execution with checkpointing, resumption, and progress tracking for tasks that run for hours or days.
Agent Fleet Scaling
Infrastructure for running hundreds of parallel agent instances with queue management, resource allocation, and cost controls.
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LLM Fine‑Tuning & Custom Model Development
When prompt engineering and RAG reach their limits, fine‑tuning creates a proprietary model that embodies your domain expertise, communication style, and task‑specific reasoning capabilities. SourceMash trains domain‑specific LLMs on your enterprise data — from legal and medical knowledge to customer support tone and technical documentation — using LoRA, QLoRA, DPO, and RLHF techniques that achieve frontier‑model performance on your specific tasks at a fraction of the inference cost, while keeping your training data entirely within your controlled infrastructure.
Training Data Curation & Preparation
High‑quality fine‑tuning begins with high‑quality training data. We design data curation pipelines that collect, clean, deduplicate, balance, and format your enterprise data — using LLM‑assisted data generation to augment scarce real examples, quality filtering to remove poor‑quality samples, and curriculum learning strategies that sequence training for maximum model improvement with minimum data requirements.
Parameter‑Efficient Fine‑Tuning (LoRA / QLoRA)
Efficient fine‑tuning of large language models using LoRA and QLoRA — adapting 7B to 70B+ parameter models on enterprise GPU hardware by training only a small fraction of parameters while achieving near‑full fine‑tuning performance. We select optimal rank, alpha, and target module configurations, apply 4‑bit quantisation where appropriate, and validate that adapters merge cleanly without catastrophic forgetting.
RLHF, DPO & Preference Alignment
Align fine‑tuned models with human preferences and enterprise values using Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimisation (DPO), and ORPO — producing models that not only know your domain but behave in ways that reflect your organisational standards, on‑brand communication style, and customer expectations consistently.
Distributed Training Infrastructure
Design and provision the GPU training infrastructure for large‑scale fine‑tuning — multi‑GPU distributed training with DeepSpeed ZeRO and FSDP, efficient data loading and preprocessing pipelines, gradient checkpointing for memory efficiency, and cloud‑based training job orchestration on AWS, Azure, and GCP with cost‑optimised spot instance strategies.
Model Evaluation & Benchmarking
Rigorous evaluation of fine‑tuned models — running domain‑specific holdout test suites, comparing against base models and GPT‑4o on your actual use cases, measuring catastrophic forgetting on general capabilities, evaluating hallucination rates, and producing model evaluation reports that give stakeholders the evidence they need to approve production deployment with confidence.
Model Quantisation & Deployment Optimisation
Post‑training optimisation for efficient production serving — applying GPTQ, AWQ, and GGUF quantisation to reduce model memory footprint by 4‑8x without significant quality degradation, merging LoRA adapters into base weights for deployment simplicity, and optimising models using TensorRT‑LLM and speculative decoding for maximum throughput and minimum latency at production scale.
Fine‑Tuning Core Capabilities
Model Versioning & Registry
Full model lifecycle management with versioning, A/B testing between model versions, and rollback capabilities for production deployments.
Private Training Environment
All fine‑tuning performed within your VPC — training data never leaves your security perimeter, ensuring full data sovereignty.
Continuous Fine‑Tuning Pipelines
Automated retraining pipelines that update your fine‑tuned models as new domain data accumulates, with quality gating before promotion.
Multi‑Task Fine‑Tuning
Single model fine‑tuned for multiple enterprise tasks simultaneously — maximising capability per parameter and reducing serving costs.
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Multimodal AI & Generative Media
The most powerful GenAI applications reason across multiple modalities simultaneously — understanding images, documents, audio, and video alongside text to deliver capabilities that single‑modality systems cannot match. SourceMash builds production systems that see, listen, and understand the full richness of enterprise data: vision‑language models that analyse product images, multimodal document intelligence that reads tables and charts, AI image generation for scaled creative production, and enterprise video AI for your visual content at scale.
Vision‑Language Model Integration
Production systems built on GPT‑4o Vision, Claude Vision, Gemini Pro Vision, and open‑source VLMs (LLaVA, InternVL, Phi‑3 Vision) that understand and reason about images, charts, diagrams, screenshots, and documents — enabling visual QA, product image analysis, receipt and invoice understanding, quality inspection reporting, and AI‑powered accessibility features for users who cannot see visual content.
AI Image Generation & Brand Asset Production
Enterprise AI image generation pipelines — fine‑tuning FLUX1, Stable Diffusion XL, and DALL‑E 3 on your brand assets, product photography, and visual style guide to generate consistent, on‑brand imagery at scale: product lifestyle shots, marketing creatives, social media visuals, and design variations without manual design intervention.
Multimodal Document Intelligence
Intelligent document understanding systems that extract meaning from the full visual structure of documents — parsing tables, charts, graphs, form fields, signatures, stamps, and mixed text‑image layouts using LayoutLM, Donut, and vision‑language models that understand both content and spatial relationships within complex enterprise documents that traditional OCR and NLP pipelines cannot handle.
Video AI & Understanding
Multimodal video understanding systems — training video content for AI‑powered search, manufacturing quality inspection from video feeds, meeting transcription and action item extraction from recordings, retail footfall analysis, and social media video intelligence at scale using Gemini Video, Video‑LLaVA, and custom video encoders with temporal grounding capabilities.
Audio & Speech AI
Production audio AI combining speech recognition, speaker identification, emotion analysis, and language understanding — for call centre AI that transcribes and scores customer calls in real time, meeting intelligence that extracts decisions and action items from recordings, and voice‑commanded enterprise applications combining STT, LLM reasoning, and TTS for fully voice‑native AI experiences.
Multimodal Search & Product Discovery
Unified multimodal search systems that let users find what they need using any combination of text, images, and video — enabling visual product search, cross‑modal retrieval, and AI‑powered discovery experiences that understand aesthetic preferences and visual similarity in ways keyword search fundamentally cannot, driving measurable improvements in product discovery conversion rates.
Multimodal AI Core Capabilities
Brand‑Safe Image Generation
Fine‑tuned models trained on your brand assets ensuring every generated image aligns with your visual identity guidelines.
AI‑Powered Accessibility
Automated alt‑text generation, image description, and visual content narration making digital assets accessible to all users.
Multilingual Multimodal
Vision‑language systems that understand and respond in 30+ languages for global deployment of visual AI applications.
High‑Throughput Media Processing
Scalable batch pipelines capable of processing millions of images or hours of video daily with GPU‑optimised infrastructure.
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GenAI Guardrails & Responsible AI Governance
Deploying Generative AI responsibly is not optional — it is the difference between GenAI that creates value and GenAI that creates liability. SourceMash’s Guardrails & Governance practice designs the safety, compliance, and governance architecture that makes your GenAI systems trustworthy by design: output validation layers, hallucination detection, toxicity filtering, prompt injection defence, PII protection, EU AI Act compliance frameworks, and the human oversight mechanisms that keep your organisation in control of its AI.
Output Validation & Hallucination Mitigation
Multi‑layer output validation architectures that detect and block hallucinated, factually incorrect, and policy‑violating LLM responses — using fact‑checking against retrieval sources, NLI‑based contradiction detection, confidence calibration, structured output schema validation, and LLM‑as‑judge quality gates that maintain response accuracy standards at every request volume with sub‑100ms validation overhead.
Prompt Injection Defence
Comprehensive prompt injection and jailbreak defence — detecting direct injection attacks, indirect injection attacks embedded in retrieved documents, and multi‑turn manipulation attempts using classification‑based detectors, input sanitisation, and layered defence‑in‑depth architectures that maintain security without degrading the legitimate user experience for production GenAI applications.
PII Detection & Data Privacy
Automated PII detection, pseudonymisation, and redaction in both inputs and outputs — protecting names, addresses, financial account numbers, health identifiers, and other sensitive data from appearing in LLM prompts or completions using Presidio, AWS Comprehend, and custom NER models trained on your specific data types, with configurable policies for different sensitivity levels and regulatory contexts.
EU AI Act & Regulatory Compliance
End‑to‑end EU AI Act compliance implementation — conducting AI system risk classification, preparing technical documentation and conformity assessments for high‑risk AI systems, implementing required human oversight mechanisms, maintaining AI system logs and audit trails, and building AI governance committee structures and review processes that regulators expect from responsible enterprise AI adopters.
AI Audit Trails & Explainability
Comprehensive audit logging and explainability infrastructure — capturing every request, response, retrieved context, model version, user identity, and decision trace with tamper‑evident logging, retention policies, and query interfaces that allow compliance teams to reconstruct any AI interaction for regulatory inquiry, legal discovery, or internal investigation without system performance impact.
AI Governance Frameworks & Policy
Organisational governance structures, policies, and processes for responsible AI adoption at enterprise scale — including AI use case review boards, model risk management frameworks, vendor due diligence checklists, acceptable use policies, employee AI literacy programmes, and AI incident response playbooks that ensure your organisation deploys AI confidently while maintaining board‑level accountability.
Guardrails Core Capabilities
Content Moderation
Real‑time toxicity detection, hate speech filtering, and brand safety screening for user‑facing GenAI applications.
Copyright & IP Protection
Output screening to prevent reproduction of copyrighted content, training data memorisation detection, and IP infringement risk mitigation.
Bias Monitoring
Continuous monitoring of GenAI outputs for demographic bias, stereotyping, and unfair treatment across user groups with alerting.
Human‑in‑the‑Loop Escalation
Intelligent confidence thresholds that route low‑certainty or high‑stakes AI responses to human review queues before delivery.
Our Generative AI Technology Stack
We work across the world's leading foundation models, orchestration frameworks, vector databases, evaluation tools, and cloud AI platforms — choosing the right tool for each problem, not forcing every solution into a single vendor's ecosystem.
Our GenAI Delivery Process
A structured, iterative, and risk-aware GenAI delivery methodology — from use case validation and architecture to production deployment and continuous improvement, with responsible AI embedded at every stage.
GenAI Use Case Discovery & Feasibility
We map your business challenges to GenAI techniques, assess data readiness, estimate ROI with honest productivity projections, and define success metrics tied to business outcomes. We also conduct a build‑vs‑buy evaluation and flag regulatory risks upfront before any design or engineering work begins.
Architecture Design & Model Selection
Our GenAI architects design the system architecture — selecting foundation models, retrieval strategies, orchestration frameworks, vector databases, and deployment infrastructure for your requirements, with PoC experiments validating key architectural decisions before committing to a full build.
Agile GenAI Development Sprints
Development proceeds in 2‑week sprints with working demos at the end of every sprint so you can provide real feedback and direction is adjusted early — avoiding late‑stage surprises.
Evaluation, Red‑Teaming & Safety Testing
Before production deployment, we run systematic evaluations — measuring output quality on domain benchmarks, conducting red‑team testing, validating hallucination rates, and testing prompt‑injection scenarios. No GenAI system ships without passing safety gate criteria.
Phased Production Deployment
GenAI systems are deployed using phased rollout strategies — shadow mode, limited beta, and gradual traffic expansion — with real‑time quality monitoring, A/B testing against baselines, and automatic rollback capabilities for risk‑free launches.
Continuous Improvement & Managed Services
Post‑launch, we monitor quality, cost efficiency, and user feedback — running monthly evaluation reviews, refining prompts and retrieval, and incorporating new foundation model capabilities as they emerge. Properly managed GenAI systems improve continuously over time.
GenAI That Delivers in the Real World
Real outcomes from production GenAI deployments — see how SourceMash has helped enterprises turn generative AI ambitions into measurable business results.
RAG‑Powered Developer Assistant Reduces Support Ticket Volume by 65% for SaaS Platform
Fine‑Tuned Legal LLM Cuts Contract Review Time by 78% for Global Law Firm
AI Shopping Agent Drives 33% Increase in Average Order Value for Fashion Retailer
What Our Clients Say
Trusted by technology leaders, product executives, and innovation teams worldwide — here is what they say about building production GenAI with SourceMash.
We had tried building a RAG system internally and the hallucination rate was unacceptable. SourceMash rebuilt our architecture with hybrid retrieval, cross‑encoder reranking, and output validation — and took answer accuracy from 61% to 92% in eight weeks. That is the difference between a prototype that embarrasses you and a product customers trust.
We needed our LLM to understand the specific language of commercial contract law. SourceMash fine‑tuned Llama 3 on our case archive, deployed it on‑premise so client data never leaves our environment, and delivered a system our senior lawyers describe as genuinely useful. A 78% reduction in contract review time is not a rounding error.
The AI shopping agent SourceMash built combines text chat with visual product search in a way that feels genuinely magical to our customers. A 33% increase in average order value and 80% reduction in cart abandonment in the first 90 days. Their GenAI engineering depth — from agent reasoning to guardrails — is exceptional.
Certified. Trusted. Recognised.
Our Generative AI team combines academic research depth with production engineering rigour — backed by official partnerships and certifications from the world's leading AI platform providers.
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Perspectives, research, and practical guidance from our enterprise technology experts.
