01 / FEATURE
Semantic chunking with Jina v3
Jina Embeddings v3 breaks your documents into meaningful chunks that preserve context across long-form content.
Production-grade Generative AI, grounded in your data, under your control.
Generic LLMs are 30% accurate on your domain. They hallucinate, leak data, and run slow. We build RAG, knowledge graphs and fine-tuned models that hit 97% on domain tasks, PDPA-compliant, on your infrastructure.
The problem
Generic LLMs don't work for enterprise.
Off-the-shelf models are trained on the open internet, not your domain. Your customer data is private, confidential, regulated. You can't send it to a third-party API without a compliance violation.
But if you stand a model up locally without grounding, it doesn't know your data either. It guesses. It gets it wrong roughly 30% of the time. The result is a familiar one: impressive demos, useless systems, nothing in production.
And the cost of wrong answers is real, data leakage, hallucinations users trust, ten-second latency on long documents, and a model whose reasoning you can't inspect or fix.
Our approach
Grounded GenAI with Retrieval-Augmented Generation.
The LLM doesn't guess. It retrieves relevant context from your data first, then generates answers grounded in that context. 97% accuracy on domain tasks. Hallucinations down 90%. Sub-2-second responses. Data stays private.
The technology stack.
02 / FEATURE
Knowledge graphs on Neo4j
For structured relationships and hierarchies, we layer in knowledge graphs, sharper retrieval, better multi-hop reasoning.
03 / FEATURE
Multi-LLM orchestration
Route queries to the right model, OpenAI, Anthropic, or open-source Llama/Mistral, based on accuracy, cost and latency budgets.
04 / FEATURE
Re-ranking & multi-hop retrieval
We don't trust the first ten vectors. Re-ranking and multi-hop retrieval push accuracy into the high 90s on real corpora.
05 / FEATURE
Optional fine-tuning
For highest accuracy, we fine-tune open-source models on your domain, bespoke vocabulary, lower ongoing cost, full control.
06 / FEATURE
Eval harness from day one
100+ test cases pre-launch. Automated drift detection post-launch. 1% of queries human-reviewed monthly. You see the numbers.
The Framework
How we build it, FORGE-aligned, four phases.
From data audit to live system, with accuracy and compliance instrumented at every step.
PHASE 01
ASSESS
Audit your data and identify viable LLM use cases. Compliance assessment for PDPA and data residency. Tool selection grounded in your stack and budget.
Deliverable
GenAI roadmap & tool recommendations
PHASE 02
ARCHITECT
Design the RAG pipeline end-to-end, embedding model, chunking strategy, retrieval topology, LLM selection, evaluation metrics, and access controls.
Deliverable
RAG architecture & tool configuration
PHASE 03
BUILD
Implement the pipeline, index your corpus, build the API layer, ship monitoring dashboards, and fine-tune if the accuracy bar requires it.
Deliverable
Production RAG system + dashboards
PHASE 04
OPERATE
Drift detection, usage and cost monitoring, continuous improvement against new data, and fine-tuning iterations as your domain evolves.
Deliverable
Live system + accuracy SLAs
Where grounded GenAI earns its keep.
Five patterns we've shipped across ASEAN, each grounded on the client's own corpus, each measured against a hard accuracy bar.
Financial services support
Customer support assistant grounded in account history, product docs and FAQs. First-contact resolution targets above 95%.
>95% target
Clinical knowledge assistant
Clinicians find relevant research, guidelines and patient context in seconds, not for diagnosis, but to support faster, better-informed decisions.
>98% target
Retail recommendation with reasoning
Product recommendations grounded in catalogue, history and reviews, with explanations the customer can actually read.
>90% target
Manufacturing manuals & specs
Technicians query manuals, safety docs, maintenance logs in plain language. Downtime drops because answers come back instantly.
>95% target
Government & public services
Citizen-facing Q&A grounded in agency policy and documentation. Data never leaves agency servers.
Local deploy
Internal knowledge base
Replace the broken intranet search. Employees ask in natural language; answers come back with citations to source docs.
Cited answers
Grounded RAG vs. an off-the-shelf LLM call.
Why we don't ship raw API wrappers, and why our clients don't either.
Status Quo
Generic LLM API
- ~30% accuracy on your specific domain
- Hallucinations on edge cases, no warning
- Customer data sent to third-party, compliance risk
- 10s+ latency on long documents
- Confident answers, no traceable source
- Locked to one provider, one pricing model
The EIS Way
EIS RAG implementation
- 97% accuracy on domain-specific tasks
- Hallucinations reduced ~90% via grounding
- Data stays on your infrastructure (PDPA-compliant)
- <2s response times, even on long documents
- Citations to source documents on every answer
- Swap the underlying LLM in a day, vector store stays
Our internal knowledge base was giving wrong answers 30% of the time. EIS rebuilt it with RAG and Neo4j knowledge graphs. Accuracy hit 97% within the first month, and our support team's resolution time dropped by half.
Priya Ramanathan · VP of Digital Operations · Regional Insurance Group
Compliance and data privacy, by design.
PDPA-aligned data handling, local deployment in Singapore or on your servers
Role-based access controls and audit logs on every query
Encryption in transit and at rest, end to end
Respects PDPA deletion rights, data removal flows through to vector store
Caching of common queries to control LLM API spend
Open-source model option, full control, lower ongoing cost
FAQ
Frequently asked
What CTOs and product leads ask before they commit, and what we answer.
Q01What if we don't have our data indexed yet?
That's the norm. Indexing, chunking strategy, embedding model, vector database choice, is part of the ARCHITECT phase. We design and run the pipeline; you don't have to figure it out first.
Q02How much does RAG cost to run in production?
Depends on query volume and model size. Vector database is a fixed monthly cost; LLM is per-query if you use a hosted API, or fixed compute if you self-host open-source. We'll cost it for your specific volume during ASSESS.
Q03Can we use open-source models instead of OpenAI?
Yes. We support Llama, Mistral and other open-source models. You trade a small accuracy gap on some tasks for lower cost and full control. Many regulated clients require this.
Q04How do we handle new LLM versions?
RAG insulates you. The vector store and retrieval pipeline don't change. The LLM is swappable in a day, we re-run the eval harness against the new model, you decide whether to upgrade.
Q05How do we prevent hallucinations?
Four things: clean data in the vector store, retrieval that returns the right context, prompts that ground the model, and an evaluation harness that catches regressions. Fine-tuning if the bar still isn't met.
Book a generative AI assessment
30-minute call. We'll review your data, your accuracy bar, and your compliance constraints, and tell you whether RAG, fine-tuning, or both is the right shape.
Book assessment
30 minutes · reply within 1 business day