Perimattic builds production-grade Retrieval-Augmented Generation systems that connect your enterprise data to large language models — delivering accurate, hallucination-free AI responses grounded in your proprietary knowledge.
RAG Stack We Build On — LangChain, LlamaIndex, Pinecone, Weaviate, Vector Search, and modern retrieval frameworks
Retrieval-Augmented Generation (RAG) is an AI architecture that combines large language models with external knowledge retrieval to produce accurate, contextually grounded responses. Instead of relying solely on the model's training data — which has a fixed cutoff date and no knowledge of your organisation — a RAG system fetches relevant documents from your knowledge base at query time and provides them as context before the LLM generates a response. The result is AI that answers from your data, not from a generalised approximation of it.
The practical implication is significant. A standard LLM asked about your compliance policy will produce a confident, plausible answer based on training data that has never seen your policy. A RAG system retrieves the actual policy document, cites the relevant clause, and flags when the retrieved evidence is insufficient to answer confidently. That is the difference between a language model and an enterprise knowledge system.
Perimattic designs and builds production-grade RAG systems from the ground up: ingestion pipelines that process your documents at scale, vector databases tuned for your retrieval patterns, hybrid search combining semantic and keyword matching for maximum recall, and evaluation frameworks that measure accuracy objectively. Every system is built with enterprise security, role-based access control, and the monitoring infrastructure needed to keep retrieval quality high as your data evolves.
Knowledge source
Static training data with a fixed cutoff date
Knowledge source
Your live documents, updated in real time
Hallucinations
Confabulates confident but incorrect answers
Hallucinations
Cites sources, declines when no evidence found
Domain specificity
Generic, no knowledge of your organisation's data
Domain specificity
Grounded in your proprietary knowledge base
Auditability
No source attribution or reasoning trail
Auditability
Every answer traces back to a retrieved document
Data control
Your queries and data may leave your infrastructure
Data control
On-premise or private cloud, fully within your control
The distinction matters most in high-stakes domains: clinical decision support, regulatory compliance, legal research, financial analysis. These are exactly where the accuracy gap between a generic LLM and a RAG system grounded in your data determines whether the system is deployable at all.
Seven specialist service lines covering the full RAG lifecycle — from knowledge base architecture to production monitoring.
End-to-end design and build of Retrieval-Augmented Generation pipelines: ingestion, chunking, embedding, retrieval, reranking, and LLM generation. Every pipeline is built for production accuracy from the first sprint.
Design and build vector databases, chunking strategies, and retrieval pipelines optimised for your content type and query patterns. We select the right architecture for your data volume, latency requirements, and access model.
Advanced embedding models and hybrid search — combining vector similarity with BM25 keyword matching — for precise document retrieval across large, heterogeneous knowledge bases.
Role-based access control, data encryption at rest and in transit, and audit logging that meet SOC2, HIPAA, and enterprise compliance requirements. Your data stays within your infrastructure.
Iterative tuning of chunking strategies, embedding models, and re-ranking layers to maximise answer accuracy and relevance. We establish evaluation baselines so improvements are measured, not assumed.
Clean REST and GraphQL APIs that integrate RAG capabilities into your existing applications, portals, and workflows. We handle authentication, rate limiting, and fallback logic as part of every integration.
Track retrieval quality, answer accuracy, latency, and user satisfaction in real time with automated alerting. Includes LLM call tracing, embedding drift detection, and accuracy evaluation frameworks.
A structured six-stage process from free scoping session to live deployment and ongoing retrieval optimisation.
We start by deeply understanding your business goals, existing systems, and document corpus. Our team audits your data sources, defines retrieval accuracy targets, and identifies the use cases that will deliver the fastest measurable return.
Our architects design a scalable, secure RAG architecture tailored to your needs. We select the optimal LLM, embedding model, and vector database, define data flows, plan integrations, and create a detailed project roadmap.
We build a working RAG prototype against a real slice of your data to validate retrieval accuracy and surface any data quality or chunking challenges before the full production build begins.
We build the full production RAG pipeline: ingestion, chunking, embedding, indexing, retrieval, reranking, and generation. We connect the system to your existing applications, databases, and APIs.
Rigorous evaluation of retrieval accuracy and answer quality against agreed baselines, including adversarial and edge-case queries. We run load testing, security scanning, and access control validation before go-live.
Zero-downtime deployment with monitoring dashboards, retrieval quality alerting, and reindexing pipelines. We remain available for the first weeks in production, resolve any issues, and plan the next optimisation iteration.
We start by deeply understanding your business goals, existing systems, and document corpus. Our team audits your data sources, defines retrieval accuracy targets, and identifies the use cases that will deliver the fastest measurable return.
Our architects design a scalable, secure RAG architecture tailored to your needs. We select the optimal LLM, embedding model, and vector database, define data flows, plan integrations, and create a detailed project roadmap.
We build a working RAG prototype against a real slice of your data to validate retrieval accuracy and surface any data quality or chunking challenges before the full production build begins.
We build the full production RAG pipeline: ingestion, chunking, embedding, indexing, retrieval, reranking, and generation. We connect the system to your existing applications, databases, and APIs.
Rigorous evaluation of retrieval accuracy and answer quality against agreed baselines, including adversarial and edge-case queries. We run load testing, security scanning, and access control validation before go-live.
Zero-downtime deployment with monitoring dashboards, retrieval quality alerting, and reindexing pipelines. We remain available for the first weeks in production, resolve any issues, and plan the next optimisation iteration.
Select an industry to see how RAG systems reduce manual research and deliver accurate, source-cited answers at scale.
RAG systems transform clinical knowledge retrieval, enabling physicians and researchers to get evidence-based answers from proprietary medical literature, patient records, and clinical guidelines in seconds.
RAG systems transform clinical knowledge retrieval, enabling physicians and researchers to get evidence-based answers from proprietary medical literature, patient records, and clinical guidelines in seconds.
Financial institutions use RAG to navigate complex regulatory landscapes and give advisors, compliance officers, and analysts instant access to the right information from vast document repositories.
Law firms and legal teams use RAG to search contracts, case law, and precedent at scale, dramatically reducing research time while improving accuracy and source traceability.
Manufacturing organisations use RAG to make technical knowledge instantly accessible — from maintenance manuals to quality standards — reducing downtime and improving production consistency.
Retail teams use RAG to give customers and agents accurate, product-specific answers drawn from catalogues, policies, and inventory data — at scale and without hallucination.
Technology companies use RAG to power developer documentation portals, internal knowledge bases, and support systems that stay current with product changes without retraining.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade RAG engineering from demo-quality builds.
Every RAG system we build includes retrieval evaluation frameworks, monitoring dashboards, security controls, and fallback logic from the first sprint. We do not build demos that need to be re-engineered before they can go live.
We have delivered RAG projects across healthcare, legal, financial services, manufacturing, and technology. Our team understands the domain-specific retrieval challenges — terminology, document structure, access controls — that determine whether a RAG system is useful in practice.
Fixed-scope projects with clear milestones, regular demos, and no surprise invoices. You see working software at every stage and always know exactly where retrieval accuracy stands against the agreed baseline.
We recommend the best LLM, embedding model, and vector database for your use case — not the platform that maximises our margin. Our vendor-neutral approach means you avoid lock-in and get a system that can evolve as the technology landscape changes.
“Unlike boutique dev shops that build to spec without strategic context, or large consultancies that advise without shipping, Perimattic brings both capabilities to every engagement.”
RAG (Retrieval-Augmented Generation) development is the process of building AI systems that combine large language models with external knowledge retrieval. Instead of relying solely on a model's training data, a RAG system fetches relevant documents from your knowledge base at query time, provides them as context to the LLM, and generates a response grounded in that retrieved content — dramatically reducing hallucinations and enabling AI that stays current with your latest information.
Fine-tuning permanently updates a model's weights by training it on your data — it is best for teaching the model a new style, format, or domain behaviour. RAG retrieves relevant documents at inference time and provides them as context — it is best for grounding answers in specific, frequently updated knowledge. For most enterprise knowledge retrieval use cases, RAG is faster to build, cheaper to maintain, and more auditable than fine-tuning. The two approaches can also be combined.
Standard LLMs generate responses based on patterns learned during training, which means they can confabulate plausible-sounding but incorrect information when asked about specific facts. A RAG system retrieves actual documents before generating a response, so the model is constrained to produce answers supported by retrieved evidence. When no relevant document is found, the system can be configured to decline to answer rather than fabricate — which is critical in high-stakes domains like healthcare, legal, and finance.
We build data ingestion pipelines that handle PDFs, Word documents, PowerPoint files, plain text, HTML, Markdown, structured databases (SQL), JSON/XML APIs, SharePoint, Confluence, Notion, Google Drive, and email archives. The ingestion pipeline extracts, cleans, chunks, and embeds content from these sources into your vector database. For specialised document types — medical records, legal filings, financial statements — we build custom parsers that preserve the semantic structure critical for accurate retrieval.
RAG development typically ranges from USD 25,000 to USD 120,000 depending on data volume, source complexity, accuracy requirements, and the number of integrations. A focused single-domain RAG system starts from around USD 25,000. An enterprise-grade multi-source RAG platform with custom retrieval pipelines, role-based access control, and production monitoring is typically USD 60,000 to USD 120,000. We scope every engagement before quoting so there are no surprises.
Most RAG projects take six to fourteen weeks from kickoff to production, including data preparation, pipeline development, and optimisation. A proof-of-concept RAG system on a well-defined document corpus typically takes two to four weeks. A production system with full integration, role-based access, and monitoring typically takes eight to fourteen weeks depending on data complexity and source diversity. We provide a more accurate estimate after the free scoping session.
We work with Pinecone, Weaviate, Qdrant, Milvus, pgvector, and Redis — selecting the right database based on your scale requirements, infrastructure preferences, query patterns, and budget. For on-premise requirements, we favour pgvector or Qdrant. For large-scale cloud deployments, Pinecone or Weaviate are typically optimal. We make a recommendation after the free scoping session.
Yes. We build fully on-premise RAG systems that never send your data to external services. This uses self-hosted LLMs (such as LLaMA 3 or Mistral via Ollama), on-premise vector databases (Qdrant or pgvector), and private embedding models (Sentence Transformers). This architecture is common in healthcare, legal, financial services, and government contexts where data sovereignty is a hard requirement.
Yes. We have deep experience connecting RAG systems to ERPNext, Salesforce, HubSpot, SAP, SharePoint, Confluence, and custom REST APIs. Integration means both ingesting data from these systems into the RAG knowledge base and surfacing RAG responses within the interfaces your team already uses. We handle authentication, incremental re-indexing as data changes, and access control that mirrors your existing permissions model.
We establish evaluation metrics before development begins: precision@k, recall@k, mean reciprocal rank, and business-specific accuracy scores. We build a labelled evaluation dataset from representative queries, run automated evaluation pipelines against it, and set a minimum accuracy threshold as a go-live criterion. In production, we instrument the system to log every query, retrieved chunk, and user feedback signal, and we monitor retrieval quality drift over time using automated alerting.
Tell us about your knowledge base and the questions your team needs answered. We will show you exactly how a RAG system can reduce manual research, eliminate hallucinations, and deliver a measurable return in the first quarter.