Get Started with your requirements and primary focus, that will help us to make your solution
We build retrieval-augmented generation systems and custom LLM applications on your own data: permission-aware, measured for accuracy, and ready to grow into agents that act.
Trusted on five-year contracts by a leading Indian bank and a Dubai government entity.
RAG development is building systems that retrieve relevant passages from an organisation's own documents and data at query time, then have a language model answer using only that material, with citations. It keeps answers current and checkable without retraining the model.
An LLM development company designs, builds and runs applications on large language models: choosing the model, connecting it to company data through RAG, fine-tuning where it helps, measuring quality with evaluations, and operating it securely in production, often on private infrastructure.
Chunk, embed, retrieve the top matches, generate. A fast baseline for small, clean corpora.
Keyword and vector search combined, so exact terms like product codes are never missed.
A second model reorders results by relevance before generation, lifting answer quality.
Retrieval over a knowledge graph of entities and relations, for questions that span many documents.
An agent plans its searches, checks results and retrieves again until the answer is supported.
Claude, GPT, Llama and other models compared on your tasks for quality, cost, latency and policy.
Adapting models to your domain, format or tone when prompting and retrieval are not enough.
Training smaller, cheaper models from larger ones for high-volume or edge workloads.
Test sets built from real questions, scoring accuracy, grounding and safety before every release.
Versioned prompts and models, monitoring, cost tracking and feedback loops in production.
Open-weight models on your cloud, on-prem or sovereign infrastructure. See NexaStack private LLM options.
Most enterprise systems combine them. This is how we decide where to start.
| Dimension | RAG | Fine-tuning | Long context |
|---|---|---|---|
| Best for | Answering from large, changing knowledge | Teaching a style, format or narrow skill | Reasoning over a few long documents at once |
| Freshness | Updates as soon as sources change | Fixed until the next training run | Current for whatever you paste in |
| Citations | Built in | Not available | Possible, less precise |
| Access control | Filtered per user at retrieval | Hard: knowledge sits in the weights | Depends on what is loaded |
| Cost profile | Indexing plus retrieval per query | Upfront training, cheaper inference | High per query at large sizes |
| Often combined with | Reranking, evals, agents | RAG, for knowledge | RAG, to pick what to load |
Documents are filtered by each user's existing access rights before anything reaches the model, so no one sees what they couldn't open.
Incremental indexing keeps answers aligned with the latest policy, contract or product version, with sources dated.
Every answer links to the passages it used, so reviewers and auditors can check it in seconds.
Automated grounding checks flag claims the sources don't support, before release and continuously in production.
Claude, GPT, Gemini, Llama, Mistral and other open-weight models.
Model-agnostic, so you can switch without rebuilding.
pgvector, Pinecone, Weaviate, Elasticsearch and OpenSearch.
Plus knowledge graphs from ElixirData Context OS.
LangChain, LlamaIndex, LangGraph and MCP.
Deployed on AWS, Azure, private cloud or on-prem.
RAG assistants answer questions with citations. Low risk, fast value.
The system suggests an action, such as a refund or a ticket route, and a person approves it.
Agents take bounded actions in your systems, with decision boundaries and a decision trace. See AI agent development.
The retrieval layer, evals and permissions you build for RAG carry straight into agents, so nothing is thrown away. Starting with a conversational front end? Talk to us about AI chatbot development.
Pick the questions, sources, users and the quality bar.
Connect sources, chunk, embed and apply access rights.
Score answers on a test set from real questions.
Release to a user group and collect feedback.
Monitor quality and cost, add sources, extend to agents.
Cost depends on the number and type of sources, access-control complexity, quality targets and where it is hosted. A scoped pilot on one use case is the fastest way to a firm number.
Use RAG when answers must come from large or changing knowledge, with citations and access control. Fine-tune when you need a consistent style, format or narrow skill. Many production systems use both.
Answers are generated only from retrieved passages, cited, and checked by automated grounding evaluations before release and in production.
Yes. Retrieval filters results by each user's existing access rights, so the model never sees content the user couldn't open.
Claude, GPT, Gemini, Llama, Mistral and other open-weight models, chosen per task on quality, cost, latency and policy.
Yes. We deploy open-weight models on your private cloud, on-prem or sovereign infrastructure when data can't leave your environment.
A pilot on one use case typically comes first, followed by scaling to more sources and users. Timeline depends on source readiness and access rules, and is set during scoping.
You own your data, indexes, prompts and configurations. Engagement IP terms are confirmed in the statement of work with your legal team.
We'll pick the use case, map the sources and access rules, and agree the quality bar your answers must meet.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
