Story
Hear from the founder
Mattias Westergren on why Enterprise First exists and what we’re building.
Private Enterprise AI
Save money. Control data. Become more efficient.
Enterprise First helps organizations move from demos to repeatable workflows and business output. We map AI spend, cloud, SaaS, rework, manual work, sensitive data, access, traceability, and model choice before bigger investment decisions are made.
Lower cost
AI spend, cloud, SaaS, rework, and manual work
Better control
Sensitive data, access, traceability, and model choice
Measured progress
From demos to repeatable workflows and business output
The AI cost is not only token cost
The real cost appears around the model.
Runtime, SaaS, data movement, manual validation, and repeated context discovery can turn AI from an experiment budget into infrastructure cost. A large programme does not need more AI activity. It needs more progress per krona.
01
Tokens
Visible meter
02
Waiting
Queue and latency
03
Context
Rediscovery
04
Validation
Manual checks
05
Rework
Weak output
06
Capacity
Senior cleanup
07
Progress
Measured impact
Where private AI typically creates value
Save cost, control data, and turn work into repeatable workflows.
A first-pass sizing that finds the business case
We map cost, data, workflows, and risk before bigger investment decisions are made.
Rough answers are enough to identify your business case, cost drivers, platform size, and potential savings. The first pass is designed to create a decision view, not a long discovery project.
Inputs
- Users
- Workflows
- Platform status
- Current cost drivers
Sizing output
- Small / medium / large
- Platform direction
- Hardware/runtime estimate
- Private vs external split
Decision view
- Pilot scope
- Savings levers
- Risk reduction
- Implementation effort
Business effect
- Lower cost
- Better control
- Faster delivery
- Clearer investment decision
Core questions
What is already costing money?
Which data should remain private?
Which workflows can AI improve?
What can be reduced while scaling?
Start with rough answers
A first pass is enough to identify size, pilot area, cost picture, and potential savings.
Reply with rough ranges. We return a first-pass sizing: cost drivers, platform direction, pilot scope, and savings potential.
Request first-pass sizingSizing questionnaire
AI / data workflow users
1-25 / 25-75 / 75-150 / 150-500 / 500+
Area to benchmark first
One workflow / one team / one function / several teams
Main workloads
Copilots / ETL / SQL / RAG / agents / docs / compliance / tickets
Current cost drivers
Cloud compute / data platform / tokens / SaaS / consultants / manual work
Approximate annual spend
<1 / 1-3 / 3-8 / 8-15 / 15-30 / 30+ MSEK
Manual work intensity
Minimal / some / significant / major bottleneck
Data sensitivity
Low / medium / high / critical - private runtime required
What should we return?
T-shirt size / solution direction / cost drivers / savings levers / pilot scope
Platform model
Use Enterprise First as the fast productization layer, Enclave as the sovereign infrastructure partner.
This is the operating model from the primary material: Enterprise First packages the business case and delivery layer, Enclave provides sovereign AI infrastructure, and larger partners are added where access and scale are needed.
Enterprise First
Fast productization layer for sizing, use-case shaping, templates, delivery harness, and business-facing implementation.
Enclave
Preferred sovereign infrastructure partner for private AI runtime, platform capacity, and managed operations.
Larger partners
Used where access, scale, enterprise reach, or programme capacity is needed around the core package.
Design principle
No license lock-in, no token lock-in, no cloud lock-in, and low skill lock-in.
Delivery proof
From plan to shipped result on a real production codebase.
The benchmark was not a toy prompt. It tested complex delivery across architecture, AI orchestration, build behavior, deployment, and runtime validation.
MetricLeading development copilot on third-party APIEnterprise First DevAgent harness on Enclave Sovereign AI
Agent time
23 min
53 min
Context volume
~35k estimated
~1.574M measured
Build
Failed
Passed
Production
No
Yes
Runtime validation
None
Smoke-tested
Completion
15%
94%
Result
Planned / partial
Shipped
Context continuity
The harness carries work across code, schema, tools, build logs, deployment state, and runtime validation.
Progress per krona
The operating question is not more AI activity. It is whether work moves from intent to verified output.
Production orientation
Enterprise software creates value when it is changed, built, deployed, verified, and ready to operate.
Public claims should remain evidence-based: the benchmark is delivery proof for a specific production codebase, not a universal savings guarantee.
Latest from LinkedIn
Thoughts on private AI, enterprise architecture, sovereignty, and what we're building at Enterprise First.
Follow on LinkedIn
Keeping AI Deterministic
Keeping an AI co-pilot deterministic is hard when you don't control the model's context. Sovereignty helps — but you need the framework, guardrails, and a clear intention.
2 weeks ago
Don't Wait Until Christmas
Last-minute shopping is manageable, but this is different. Acting early on your tech architecture cuts bottlenecks, removes third-party dependencies, and saves money.
3 weeks ago
Sovereign AI Architecture
Reducing third-party dependencies isn't just about cost — it's about sovereignty and control over your own stack. Sovereign AI is becoming one of the most important topics.
3 weeks ago
AI i juridiken bör inte vara en principfråga
AI in legal shouldn't be a matter of principle — it should be a matter of quality. The question is where to draw the line and how to ensure deterministic outcomes.
1 month ago
Give AI Back to Your IT Department
AI was scattered across strategy decks and innovation labs. For real business value, hand it back to IT — they understand how technology actually lands in a company.
2 months ago
Why Enterprise First
We are not AI-first. We are enterprise-first.
AI is the accelerator, not the point.
The point is to make data, workflows, and decisions useful in real enterprise environments. That means sizing before big investment, controlled execution for sensitive workloads, clear ownership, and practical delivery that can be verified.
Media intelligence, semantic search, content processing, reporting, and agent workflows remain important applications. The larger offer is the production layer that lets organizations use AI without losing control of cost, data, or operating model.
Stockholm, Sweden
Legal entity: Enterprise First AB
Serving clients across the Nordics and remotely.
Enterprise delivery stance
Enterprise First AB delivers private AI and data services with Enclaveas preferred sovereign infrastructure partner where that operating model fits the customer’s risk, data, and cost profile.
Reference client
AIK Fotboll AB — official reference customer for private AI and data services.
FAQ
Common questions about private enterprise AI
Straight answers on fit, sizing, the Enclave cooperation, and how we keep public claims grounded.
Who is Private Enterprise AI for?
It is for organizations moving from demos to production, where AI spend, cloud, SaaS, rework, manual work, sensitive data, access, traceability, and model choice need a clearer operating layer.
What is the Enterprise First and Enclave model?
Enterprise First is the fast productization layer for sizing, templates, delivery harness, and business implementation. Enclave is the sovereign infrastructure partner. Larger partners can be added where access and scale are needed.
What does first-pass sizing return?
The output can include small/medium/large sizing, platform direction, hardware or runtime estimate, private-vs-external split, pilot scope, savings levers, risk reduction, implementation effort, and a clearer investment decision.
Do we need to be in Stockholm?
No. Enterprise First AB is based in Stockholm, Sweden, and we serve clients across the Nordics and remotely. Engagement models are built around your constraints and time zones.
How do we get started?
Use the contact form with rough ranges: users, area to benchmark first, main workloads, current cost drivers, annual spend range, manual work intensity, data sensitivity, and what you want returned.
How do you handle data, privacy, and regulation?
We design for controlled data handling, clear access boundaries, traceability, and EU-aligned practice by agreement. Public compliance claims are kept evidence-based and are documented per engagement.
Start with rough answers
1
Size
Users, area to benchmark first, and approximate annual spend.
2
Cost picture
Cloud, data platform, tokens, SaaS, consultants, manual work.
3
Return
T-shirt size, solution direction, cost drivers, savings levers, pilot scope.
Next step: reply with rough ranges. We return cost drivers, platform direction, pilot scope, and savings potential.
Contact
Request first-pass sizing.
Send rough ranges for users, workload, current cost drivers, manual work intensity, data sensitivity, and what you want back.
Prefer your email app? hello@enterprisefirst.ai, or open a draft with subject line only. If server email is not configured yet, Send message fills the draft for you.