Your own AI — deployed, tuned and managed
We stand up a private AI environment inside your perimeter, load it with your knowledge, train your people on it, and keep it running as a managed service. No DevOps hires, no data leaving your control.
Delivery scopes for every starting point
Pick the engagement that matches your readiness — from a two-week feasibility sprint to a fully managed private AI operation.
AI Readiness Assessment
A short diagnostic that maps your data, risk posture and highest-ROI use cases before a dollar goes into infrastructure.
- Data inventory & sensitivity mapping
- Regulatory and audit requirements
- Use-case shortlist with ROI estimates
- Recommended deployment tier
Private Environment Build
We provision and harden the environment — managed private cloud, your own dedicated network, or on-site appliance.
- Small LLM selection & hosting
- Network isolation & access control
- Identity, SSO and role permissions
- Backup, failover and recovery
Knowledge Base & Graph RAG
Your documents, contracts, decks and records ingested into a graph-aware retrieval layer that understands relationships, not just keywords.
- Document ingestion & parsing pipelines
- Entity and relationship extraction
- Cited, traceable answers
- Permission-aware retrieval
Industry Workspace Configuration
Pre-built workspaces for finance, legal, healthcare and manufacturing, tailored to your templates and terminology.
- Pre-loaded prompts & workflows
- Your document templates
- Approval and review steps
- Custom output formats
Team Enablement
Hands-on training so your analysts, associates and operators actually use the system — not just admire it.
- Role-based training sessions
- Prompt and workflow playbooks
- Internal champion coaching
- Adoption tracking
Managed AI Service
An ongoing retainer covering monitoring, model updates, new workspaces and quarterly performance reviews.
- Uptime & performance monitoring
- Model and security updates
- New use-case rollouts
- Quarterly business reviews
Built for regulated environments
Everything we deploy assumes your data is confidential, your auditors are watching, and your team has no time to babysit infrastructure.
Air-gapped option
Runs with no outbound internet path at all.
Data sovereignty
Data stays in your jurisdiction and your perimeter.
Full audit trail
Every query, source and answer logged for review.
Graph-aware retrieval
Surfaces hidden links across documents and entities.
Efficient small models
Right-sized models that run on modest hardware.
Permission-aware
Answers respect existing document access rights.
Predictable cost
Fixed infrastructure cost, no per-token surprises.
Day-1 workflows
Pre-loaded workspaces, never a blank database.
From assessment to daily production use
A structured rollout designed around procurement, security review and real adoption.
Assessment
Two weeks mapping data sources, risk requirements and the use cases with the clearest payback.
Tier & architecture
Choose managed private cloud, dedicated network or on-site appliance, and agree the security architecture with your IT team.
Provision & harden
Environment stood up, models installed, access control and logging configured, penetration-tested before any data lands.
Knowledge ingestion
Your documents parsed, entities extracted and indexed into the graph retrieval layer, with retrieval quality proven against real questions.
Workspace tuning
Industry workspaces configured to your templates, terminology and approval steps, then validated by your subject-matter experts.
Enablement & handover
Role-based training, playbooks and an internal champion, so usage sticks after we step back.
Managed operation
Ongoing monitoring, updates, new workspaces and quarterly reviews under a fixed monthly retainer.
The private AI stack
Open, self-hostable components — nothing that forces your data through a public API.
Frequently asked
Do we need our own AI engineers?
No. That is the point of the managed service. We deploy, tune and operate the environment, and train your existing team to use it. Your IT team keeps control of the perimeter; we handle the AI layer.
Where does our data actually live?
Entirely inside the tier you choose — a managed private cloud in your jurisdiction, a dedicated private network you control, or a physical appliance on your own premises. Nothing is sent to public model providers and nothing is used for training.
What is Graph RAG and why does it matter?
Ordinary retrieval finds passages that look similar to your question. Graph retrieval also maps the relationships between people, companies, clauses and documents, so the system can answer questions that span many files — like which portfolio founders share a director, or which contracts conflict with a new regulation.
How long before our team is using it?
Assessment takes about two weeks, and a first production workspace is typically live around six weeks from kick-off. On-site appliance deployments depend on hardware lead time and your security review.
What does it cost?
Cost depends on the tier, data volume and number of workspaces. We quote a fixed build fee plus a monthly managed-service retainer after the assessment, so there are no per-token surprises. Request a quote and we will scope it with you.
Can it work alongside the public AI tools we already use?
Yes. Many clients route confidential work to the private environment and keep public tools for general tasks. We help you set the policy on which data goes where, and enforce it technically.