Production AI that moves real KPIs - not just demos.
LLM assistants over your private knowledge, document AI that reads invoices and contracts, dashboards that forecast, and big-data pipelines that survive audits. AI for institutions, not toy experiments.
AI demos look magical. Production AI is the hard part.
A chatbot that hallucinates, an OCR that fails on real scans and a dashboard nobody trusts - those are project killers, not solutions.
Hallucinations and dead-end answers
Vanilla LLMs make things up. Without retrieval, citations and guardrails, the assistant becomes a liability rather than an asset.
Documents trapped in PDFs
Contracts, invoices, ID scans and forms sit in folders nobody reads. Manual entry is slow and error-prone.
Dashboards no one trusts
Numbers from different systems disagree. Reports take days to refresh. Forecasts feel like horoscopes.
AI grounded in your data. Acting on your systems.
Private RAG over your documents, document AI that actually reads scans, forecasting dashboards built on a real data pipeline - all with auditing and human override.
12 modules. Pick a use case. Scale from there.
Most institutions start with one assistant or one document workflow - then expand into the rest of the stack.
LLM Assistants
Chat with policies, manuals, archives or HR documents. Multi-turn, multi-source, with citations and human override.
RAG Knowledge Base
Private vector store over your documents and data with chunking, re-ranking, source linking and freshness guarantees.
Document AI
OCR, layout analysis, NER, classification and structured extraction for invoices, contracts, IDs and forms.
Dashboards & BI
Live KPIs, drill-downs, cohort analysis and exportable reports - tied to one consistent data model.
Data Pipelines
ETL/ELT from CRMs, ERPs, HIS, files and SaaS APIs - scheduled, retried, versioned and observable.
Data Lake / Warehouse
Postgres + S3, ClickHouse or Snowflake-class warehouses - sized to your data, partitioned for cost.
Predictive Analytics
Forecasts, churn, fraud, demand and risk models - trained on your history with explainability.
Anomaly Detection
Statistical and ML-based detection on transactions, logs, sensors and metrics - with alerts and triage.
Embeddings & Search
Semantic search over your catalog, documents, support tickets and emails - faster, smarter than keyword.
AI Agents
Tool-using agents that call your APIs to create tickets, update records and schedule - with permissions and audit.
Monitoring & Eval
Trace every prompt, response, retrieval and tool call. Continuous evals catch regressions before users do.
Admin & Audit
Role-based access, prompt history, PII redaction, retention controls and audit logs for every AI action.
Cloud, open-source, on-prem - we mix what fits.
No vendor lock-in. Swap the LLM, the vector store or the warehouse without rewriting the application.
Ask in natural language. Get cited answers.
A bilingual EN/SQ assistant inside your existing app - or as a standalone tool - that knows your policies, your data and your customers. Voice input. Streaming answers. Sources you can open.
- Voice-to-text and streaming answers on mobile
- Every answer cites the source document and page
- PII-redacted prompts when policy requires it
- One-tap escalation to a human when confidence drops
AI without the privacy trade-off.
Sensitive data never leaves your perimeter when it does not need to. Prompts, sources and outputs are auditable end to end.
- No training on your data - private models and zero data retention by default
- PII detection & redaction before LLM calls when policy requires
- Self-hosted models for sovereign deployments and regulated industries
- Prompt & tool-call audit log with replay for compliance reviews
- RBAC and content filters per role, per data source
- Human-in-the-loop for high-stakes outputs and tool actions
API speed or sovereign control - pick per use case.
Four deployment models. Most clients combine them: API for low-risk tasks, self-hosted for sensitive data.
SaaS (API models)
Frontier models via API (OpenAI, Claude). Best for prototypes and low-risk content.
- Go-live in days
- Top model quality
- Pay-per-use
Private Cloud
Models on your tenant via Azure OpenAI or self-hosted open-source on GPU VMs.
- Data stays in your account
- Region of your choice
- Managed by 5G.al
On-Premise GPU
Open-source LLMs on your servers/GPUs. Best for classified data and regulated sectors.
- Air-gap capable
- Llama / Mistral
- No vendor calls
Hybrid
Frontier API for non-sensitive, self-hosted for PII. Route per data classification.
- Smart routing
- Cost optimized
- Privacy preserved
Questions IT, legal and finance ask first
Drop the rest in the form below - we will respond within one business day.
Which LLMs do you use? Can it run on-premise?
We work with OpenAI, Anthropic Claude, Azure OpenAI and open-source models (Llama, Mistral, Qwen). Models can run via API for fastest time-to-value or self-hosted on your GPUs/CPUs for sensitive data and sovereign deployments.
How does RAG over our internal knowledge work?
We index your documents (PDF, DOCX, web, databases) into a private vector store, retrieve relevant chunks per query, and pass them to the LLM with strict citation. Every answer links back to the source so users can verify and humans stay in the loop.
What kinds of documents can you process automatically?
Invoices, contracts, medical records, identity documents, archive scans and forms. We combine OCR, named-entity recognition, classification and structured extraction - with human-in-the-loop review for high-stakes outputs.
Do you handle data privacy and PII redaction?
Yes. PII is detected and redacted before LLM calls when required. We never train on your data. Logs and prompts can be encrypted, redacted or kept fully on-premise depending on your policy.
Can the AI assistant act, not just answer?
Yes. Tool-using AI agents can call your APIs (CRM, HIS, archive, ERP) to actually create tickets, update records, schedule appointments or run reports - with role-based permissions and audit trails for every action.
How is the system priced?
Fixed implementation fee plus tiered usage by tokens or compute. Self-hosted models swap token costs for GPU costs. Pilot projects with one use case typically start small. Request a tailored quote.
Start with one AI use case. Ship it to production.
Send us a sample of your documents or data and your top use case. We will scope a pilot, demo a working prototype within 2 weeks, and a tailored proposal.