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AI / ML Integration · LLM · RAG · CV

Real AI inside your product, not a demo on a slide

LLM APIs, retrieval augmented generation, semantic search, document parsing, chatbots and computer vision integrated into the software you already run. We pick models honestly based on your data, your accuracy targets and your budget, then ship a measured, evaluable system, not a black box.

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RG INSYS LLP integrates production AI and machine learning into existing and new software products. We build with OpenAI, Anthropic Claude, Mistral and self hosted Llama 3 / Qwen for language tasks; pgvector, Pinecone, Weaviate and Elasticsearch for retrieval; YOLO, CLIP, InsightFace and the Hugging Face ecosystem for vision. Every project ships with an evaluation harness, structured outputs and a clear answer on what the model can and cannot do for your use case. UK, US, UAE and Indian clients in healthcare, insurance, recruitment, retail and SaaS rely on us to ship AI features that actually hold up in production.

What we deliver
LLM API integration, RAG pipelines, semantic search, document parsing and extraction, chatbots and assistants, classification and sentiment, computer vision, face detection, OCR, evaluation harnesses and monitoring.
Typical timeline
3 to 4 weeks for a PoC. 8 to 16 weeks for a production AI feature integrated into your existing product. Ongoing tuning on a monthly retainer.
Pricing from
$10,000 fixed price PoC. $5,000/month dedicated AI engineer with full LLM API costs passed through transparently.
Stack
OpenAI, Anthropic, Mistral, AWS Bedrock, Llama 3, Qwen, LangChain, LlamaIndex, pgvector, Pinecone, Weaviate, Elasticsearch, YOLO, CLIP, InsightFace, Hugging Face Transformers, PyTorch.
Compliance-ready for
HIPAA (with private model hosting), GDPR, SOC 2. PII redaction, prompt logging policies, audit trails and on premise model options when data must not leave your network.
What's included

Production AI, not lab experiments

🧠

LLM API integration

Clean integrations against OpenAI, Anthropic Claude, Mistral, Cohere and Bedrock. Streaming responses, tool use and function calling, structured JSON outputs, prompt caching and retry logic. Cost controls and per tenant budget caps from day one.

🔎

RAG and semantic search

Document ingestion pipelines, chunking strategies tuned to your content, embeddings (OpenAI, Cohere, BGE), vector storage in pgvector / Pinecone / Weaviate and hybrid retrieval combining BM25 with semantic similarity. Citations back to source so users can trust the answer.

📄

Document parsing and extraction

PDFs, scanned forms, contracts, invoices and emails turned into structured data. OCR via Tesseract, AWS Textract or Google Document AI, paired with LLM extraction into typed JSON schemas. Confidence scores, human in the loop review queues for low confidence outputs.

💬

Chatbots and assistants

Internal support assistants, customer facing chatbots, multi tool agents that can read your docs, query your APIs and take actions. Memory, conversation state, escalation to human, and audit logs. Built into your product, not a third party widget.

📷

Computer vision

YOLO based object detection, CLIP for image search and similarity, InsightFace for face detection and recognition, segmentation models for medical and industrial use cases. Real time pipelines on GPU or batch processing on CPU depending on cost and latency budgets.

📊

Evaluation, monitoring and guardrails

Golden dataset based regression tests, run on every deploy. Production prompt and response logging with PII redaction. LangSmith, Langfuse or custom dashboards for accuracy, latency and cost. Output guardrails (Llama Guard, custom validators) and refusal handling on sensitive prompts.

Our method

How an AI integration actually unfolds

01
Discovery and feasibility, week 1

Workshop on the user problem, data sources, accuracy targets and constraints (latency, residency, budget). Honest assessment of whether AI is the right tool. Output: written feasibility memo and PoC scope.

02
Model selection and PoC, weeks 2 to 4

Build an evaluation harness against your real data. Test 2 to 4 candidate models. Implement a working prototype with the winning model. Deliver a written report with measured accuracy, cost and latency.

03
Production integration, weeks 5 to N

Integrate the AI feature into your existing product. Structured outputs, retries, monitoring, cost caps, PII handling and human in the loop where needed. Two week sprints, demo every Friday.

04
Evaluate, tune and operate

Regression suite runs on every deploy. Live monitoring of accuracy, cost and drift. Quarterly model reviews as new versions ship. Optional retainer for prompt iteration, dataset growth and model upgrades.

Our tech stack for AI / ML integration

The AI ecosystem moves quickly, so we deliberately avoid hard couplings. Application code goes through a thin model abstraction layer (LangChain or our own typed wrappers) so the underlying provider can be swapped without rewrites. We default to mainstream tools, evaluate honestly between them, and self host only when data residency, cost or quality genuinely demands it.

OpenAI (GPT-4 / o-series) Anthropic Claude Mistral AWS Bedrock Llama 3 / Qwen (self hosted) LangChain LlamaIndex pgvector Pinecone Weaviate Elasticsearch YOLO / Ultralytics CLIP InsightFace PyTorch Hugging Face Transformers
Proof

A representative case study

Insurance · US US insurance carrier

AI document processing for an insurance claims team, 12 weeks

A US insurance carrier was drowning in unstructured PDFs (medical reports, repair estimates, police reports) on every claim. We built an AWS Textract plus Claude 3 pipeline that extracts a structured claim object, flags low confidence fields for human review, and posts directly into the existing claims platform via API. Measured 92% extraction accuracy on the held out test set, with the human review queue cut by 70%.

92%Field extraction accuracy
70%Manual review reduction
12 wksPoC to production
2 devsTotal team size

Read full case study →

Pricing

Transparent pricing for AI / ML integration

From $10,000

Fixed price 3 to 4 week PoC. Or move to a $5,000/month retainer with a dedicated AI engineer plus full LLM API costs passed through transparently.

  • Evaluation harness against your real data, not vendor demos
  • Honest model selection memo with cost and accuracy comparisons
  • Working prototype integrated into a sandbox of your product
  • Production rollout plan with infrastructure cost projections
Full pricing & engagement models →

All pricing transparent. No hidden fees. Free 48-hour written estimate.

FAQ

Common questions

Both are options. OpenAI, Anthropic and Mistral hosted APIs are the fastest path to production and the best fit when latency, scale and quality matter more than data residency. For sensitive data (healthcare, finance, internal documents) we run open source models such as Llama 3, Mixtral or Qwen on your own GPU instances or on AWS Bedrock with private endpoints. We will recommend honestly based on your data sensitivity, budget and the actual quality difference for your task.
RAG (retrieval augmented generation) lets the LLM answer using your private documents at query time. The model is unchanged; you index your content into a vector database (pgvector, Pinecone, Weaviate) and inject relevant chunks into the prompt. Fine tuning teaches the model new style or behaviour by retraining on examples. RAG is the right choice 90% of the time. It is cheaper, easier to update and lets you swap the underlying model. Fine tuning is worth it for narrow style mimicry or domain specific structured outputs.
LLMs hallucinate. We design around that fact. Every customer facing answer is grounded in retrieved context and cited back to source documents. We use structured output (JSON schemas, function calling) so the model cannot return free form text where it shouldn't. We add an evaluation harness that runs against a golden dataset on every deploy, with regression alerts when accuracy drops. For high stakes flows (legal, medical, financial) we add a human in the loop step before action is taken.
The fixed price PoC covers a 3 to 4 week engagement: requirements workshop, model selection memo with cost and accuracy analysis, a working prototype against your real data, an evaluation report with measured accuracy on a held out test set, and a written plan for the production rollout with infrastructure cost projections. You leave with a runnable demo, an honest assessment of what AI can and cannot do for your problem, and a defensible Phase 2 scope.
Yes, that is most of the work we do. We add AI features to existing Node.js, Python, Java, .NET or PHP applications via clean API services. Document parsing, semantic search, chat interfaces, smart summarisation, classification and extraction can all be deployed as additive services without touching your core schema. Your existing team keeps shipping; we sit alongside, not in the middle.
Both. We have shipped YOLO based object detection, CLIP for image search and similarity, OCR pipelines (Tesseract, AWS Textract, Google Document AI), face detection and recognition (InsightFace, AWS Rekognition) and pose estimation. Use cases include retail analytics, medical imaging triage, ID verification, document workflows and access control. We are clear about ethics and accuracy, especially around face recognition where we will refuse certain projects on principle.
We test, we do not guess. For each project we set up an evaluation harness, run the same prompts against the candidate models on your real data, and measure accuracy, latency and cost. Anthropic Claude tends to win on long context and careful reasoning. OpenAI GPT-4 and o-series win on tool use and vision. Mistral and Llama are strong on cost and self hosting. The right answer depends on your specific task, not a leaderboard.
You do, from day one. All prompts, evaluation datasets, embedding stores, fine tuned model weights and infrastructure code are committed to your repository and your cloud account. There is no vendor lock in. The same applies to the API keys, billing accounts and model provider relationships. We document the system clearly enough that any competent ML team can take it over.
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