Secure enterprise AI, built for production
We take enterprise AI from a blocked use-case to a security-approved system in production — engineered, governed and deployed inside the boundary your data is already allowed to live in.
A 30-minute working session on one workflow your security team has not approved. No deck, no obligation.
Your data never leaves the boundary you approved.
The question that stops enterprise AI is not whether the model is good. It is where the data goes, who can see what came back, and whether you can prove it afterwards. So that is what we design first.
- Permission-aware retrieval
- Inherits the ACLs your documents already carry
- Controlled egress
- You can enumerate every place a prompt can go
- Per-query audit trail
- Who asked, which model, which documents
- No training on your data
- Never used to train a model for anyone else
AI adoption is not the hard part. Approval is.
Enterprise AI projects rarely fail because a model cannot generate text. They stall when the organisation has to answer where the data goes, who is allowed to see what came back, and who owns the system on the day it breaks.
That layer — identity, permissions, integration, governance and production ownership — is what we build.
Read the breakdownNot a deck. A deployment.
The most recent one, anonymised to sector at the client’s preference.
A full learning platform with its own private AI, live in four weeks
The platform needed AI woven through the learning experience — content generation, assessment support and learner guidance — without routing student data through a third-party API. Sending learner records to a public model endpoint was not something the business was willing to sign off on.
- Private model served inside the client’s own infrastructure
- AI capability built into the platform, not attached to it afterwards
- Access control and audit logging designed in from the start
- Delivered end to end — platform, AI layer and deployment
End to end, and secure at every step.
Eight pillars covering the whole path from an idea nobody has approved yet to a system your team runs in production.
AI Strategy & Readiness
Find the use cases worth building, and the architecture that will actually get approved.
ExploreEnterprise AI Engineering
GenAI applications, ML systems and copilots built to production standards, not demo standards.
ExploreAgentic AI & Automation
Agents that take real actions in real systems, with the permissions and approvals to match.
ExplorePrivate & Secure AI
AI that runs where your data is already allowed to live.
ExploreAI Security Engineering
Threat modelling, prompt-injection defence and release controls for systems that can act.
ExploreEnterprise Knowledge & RAG
Retrieval that respects the permissions your documents already carry.
ExploreAI Governance & Quality
Evaluations, guardrails and evidence — so risk review has something concrete to read.
ExploreLLMOps & Managed AI Operations
The part most projects skip: keeping it working after launch.
ExploreThe secure AI control plane.
We separate enterprise control from the model deliberately. The model will change — probably several times. Your identity, policy, permission and audit requirements will not.
The model becomes replaceable. Governance, integration, permissions and security become the part that compounds.
- 01Enterprise usersEmployees, teams and applications
- 02Identity & accessSSO · MFA · RBAC / ABAC · source permissions
- 03iNikola secure AI gatewayPolicy enforcement · routing · isolation · egress control
- 04Data & safety controlsDLP and PII controls · prompt and response policy · audit logs
- 05Permission-aware RAG & agentsRetrieval honouring source ACLs · tools · orchestration
- 06Enterprise data sourcesSharePoint · files · databases · APIs · tickets · code · SOPs
- 07Model abstraction layerLocal SLM · open-weight LLM · VPC model · approved enterprise API
- 08Evaluation & observabilityQuality · security tests · cost · latency · risk · usage
Private Knowledge Copilot
A secure assistant over your own documents, deployed inside your boundary.
The Private Knowledge Copilot is our flagship offer and the fastest route to a governed AI system in production. It answers questions over your internal content with citations, retrieves only what the signed-in user is already permitted to see, and runs wherever your policy allows — an approved API, your own VPC, private cloud, on-premise, or fully disconnected.
- Permission-aware retrieval that inherits access control from the source system
- Citations on every answer, linking back to the originating document
- SSO, MFA and RBAC integration with your existing identity provider
- DLP and policy controls on both prompts and responses
Same control plane underneath. The vertical decides the corpus, the evaluation set and the permission model — not the architecture.
Five stages. One control plane.
Every system we deliver follows the same spine, whatever the workflow sitting on top of it.
Connect
Identity, source systems and content — wired in with the permissions they already carry.
Govern
Policy, DLP, routing rules and audit applied before anything reaches a model.
Retrieve & act
Permission-aware retrieval and agents that operate within allow-listed tools.
Serve
The application people use, in the surfaces they already work in.
Observe
Quality, cost, latency and risk measured continuously, with alerts that mean something.
Start small. Escalate only if it works.
Six steps, each one a decision point you control. Nobody is asked to commit to a platform before a single workflow has proven it can be approved.
- 01Free
Discovery
AI Approval & Architecture Review
A 30-minute working session on one blocked workflow.
No charge - 02
Assessment
Readiness & Security Assessment
Paid engagement producing the architecture and approval plan.
2–3 weeks - 03
Pilot
Fixed-scope Secure AI Pilot
One workflow, real data, controlled users, measurable criteria.
4–6 weeks - 04
Production
Departmental Deployment
Rollout, integrations, hardening and security sign-off.
Scoped - 05
Platform
Multi-use-case AI Platform
Shared control plane across business units.
Scoped - 06
Operate
Managed AI Operations
Monitoring, evaluations, updates and incident support.
Monthly
Step one costs nothing and takes thirty minutes.
One blocked workflow, mapped end to end. You leave with the architecture and the approval path whether or not you work with us.
Private does not have to mean on-premise.
We use the minimum architecture your risk profile actually requires — then design so the boundary can move later without rebuilding the application.
Approved enterprise API
Lower-sensitivity use cases where enterprise legal and security terms already cover the data class.
Customer VPC / private endpoint
Cloud-first organisations that need private connectivity and tighter boundary control.
Private cloud / on-premise
High-sensitivity data, proprietary IP, or regulatory and infrastructure constraints.
Disconnected / air-gapped
Restricted environments where no external egress is permitted at all.
Hybrid / policy router
Different data classes legitimately need different models and boundaries.
Regulated and IP-sensitive.
Organisations with real AI ambition and real constraints — where the data genuinely matters and the security question genuinely has to be answered.
Sector not listed? The constraint we solve — sensitive data that cannot leave an approved boundary — is not unique to these.
Tell us your constraintNBFC & digital lending
Credit policy and underwriting copilot
Borrower and financial data with genuine sensitivity, active AI ambition, and a security function that needs a defensible data path before anything ships.
Wealth, AMC, PMS & AIF
Compliance and research copilot
Proprietary research and investor data, compliance-heavy workflows, and small, decisive buying committees.
Insurance & TPAs
Claims and policy document copilot
Document-dominated processes over customer information, where extraction accuracy and auditability both matter.
Global capability centres
Engineering knowledge copilot
Existing AI teams that need security, platform and productionisation support rather than AI education.
Payments & fintech infrastructure
Operations and SOC copilot
High-value operations under residency and security constraints, with mature infrastructure buyers.
Pharma, biotech & CRO
Quality and SOP copilot
R&D intellectual property, regulatory knowledge and quality documentation that cannot leave a controlled boundary.
IT services, BPM & KPO
Client-isolated delivery copilot
Client confidentiality obligations that require provable isolation between engagements.
Manufacturing & engineering
Engineering and troubleshooting copilot
Engineering IP, manuals and plant boundaries, where the useful knowledge is trapped in documents and experienced people.
Service pillars, strategy through managed operations
Weeks from kickoff to a security-reviewed pilot
Deployment modes — API, VPC, private cloud, on-prem, air-gapped
Model lock-in. The routing layer is yours to change
Proof, not the whole story.
Products we have built and run ourselves. They shorten delivery on client work and demonstrate the patterns we deploy inside enterprises.
Straight answers.
The questions security teams, platform leads and business owners actually ask us in the first meeting.
Read all 24Our data is already protected in Azure OpenAI or AWS Bedrock. Why would we need anything more?
Frequently you would not, for that data class. The narrower question is whether that specific architecture is approved for this specific dataset, and whether you can produce an audit trail showing which user asked what, of which model, over which documents. Where the answer is yes, we build on your existing platform rather than replacing it.
How do you stop a RAG system from exposing documents a user should not see?
Permission-aware retrieval. The system inherits access control lists from the source system and applies them as a constraint before retrieval runs, filtered by the signed-in identity — not as a filter after results come back. Combined with SSO, RBAC or ABAC and per-query audit logging, retrieval cannot return content the user could not already open at source.
Is iNikola a home automation company?
No. Building and space automation is one product line within a broader enterprise AI business. The majority of our work is enterprise AI engineering: knowledge copilots, retrieval systems, agentic automation and secure AI platforms for regulated and IP-sensitive organisations.
How long until we see something working?
A fixed-scope pilot runs four to six weeks: week one for use-case discovery, week two for architecture and risk planning, week three to build against real data, and week four for security review and handover with a measured business KPI.
Writing from the delivery floor.
Which AI workflow does your business want that security has not approved?
Bring us that one. In thirty minutes we will map the data path, name the blockers, and tell you the architecture that clears them — or tell you it is already fine as it stands.