Banking & capital markets
MNPI · MAR · DORATrading desks and credit teams that handle material non-public information — where one mishandled prompt is a regulatory event.
Private AI infrastructure, deployed for your business.
Purpose-built hardware. Workflow-optimized models. Production-ready software. One integrated system, deployed in weeks.
Book your deployment call
→
30-minute call. No commitment. We'll assess your workload and model your costs.
● THE COST OF RENTING INTELLIGENCE
Cloud AI means variable costs, data leaving your premises, and dependency on providers who can change terms overnight. For some teams, that's not a trade-off — it's a non-starter.
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WHO WE SERVE — TEAMS THAT CAN'T OUTSOURCE TRUST
]
Healthcare & life sciences
PHI · HIPAA · GxPHospitals, payers, and pharma running on patient records and trial data that legally cannot touch a third-party inference endpoint.
Defence & aerospace
ITAR · EAR · CMMC L3Primes and tier-1 suppliers working under export-control regimes where "we used a foreign API" ends contracts and clearances.
Law & professional services
PRIVILEGE · WORK PRODUCTFirms whose entire business model rests on attorney-client privilege, audit confidentiality, or M&A secrecy that survives a subpoena.
Industrial R&D
TRADE SECRET · PATENTManufacturers, semiconductor designers, and energy firms whose process IP is the decade of competitive advantage they refuse to upload.
Public sector & critical infra
NIS2 · NERC CIP · OFFICIALAgencies, utilities, and operators of essential services where data residency is statute, not preference — and uptime is sovereign.
If your data can't leave the building, your inference shouldn't either. That's where we come in.
01 · the solved stack
Three layers, designed for each other.
Hardware, models, and software shipped as one integrated system — so your team uses it instead of maintaining it.
SILICON · IKM-1
throughput 2,400 tok/s
memory bw 4.8 TB/s
burn-in 96 h
MTBF 200k h
MODEL · ikioma-32B / v4
01 · base
open weights
02 · your tasks
domain SFT
03 · benchmarked
against suite
04 · deployed
your weights
params 32.4 B · 4-bit
context 128k
tool head 14 actions
licence perpetual
RUNTIME · Conduit / 1.4
POST
/v1/chat/completions
→ ikm-rack-01.local
"model": "ikioma-32B", "messages": [/* ... */], "tools": ["fs", "erp", "sql"]
200 · 41 ms · signed
sha256:9c12…b4a7
audit · last 4 events
- 14:02:11tool.fs.read✓
- 14:02:11tool.erp.query✓
- 14:02:12model.emit✓
- 14:02:13policy.deny×
sandbox · 14 tools
fssqlerp
gitshmail
tktcalwww
imgvc+3
API surface OpenAI / Anthropic compat
deploy single binary
You don't need to figure this out yourself. We already have.
02 · vs the alternatives
The middle path — independence without the engineering overhead.
You've already decided you want private AI. The remaining question is whether to build it yourself.
OPT.A
Cloud AI
OPT.B
DIY private AI
OPT.C
ikioma ↓ best fit
Data location
× Third-party servers
✓ Your premises
✓ Your premises
Time to deploy
~ Immediate, but dependent
× 6–12 mo
✓ weeks
Expertise required
~ API integration
× ML eng + DevOps team
✓ None
Cost model
× Variable · per-token
~ High capex + maintenance
✓ Fixed · predictable
Ongoing risk
× Vendor dependency
× Entirely on your team
✓ Supported + updated
Rate limits
× Yes
✓ No
✓ No
Model control
× None
~ Full, but complex
✓ Full, managed for you
● Comparison reflects typical mid-market deployments.
On the deployment call, we'll model your specific workload against each option.
03 · how it works
Four steps. Mostly ours.
The path is finite, the timeline is short, and you don't carry the engineering load.
-
We learn your workload.
Your data requirements, team capabilities, integration surface. We come prepared; you walk away with a costed scenario.
-
We design the system.
Hardware specified, model fine-tuned for your tasks, software stack configured against your existing systems. You approve the spec sheet.
-
We install on your floor.
Appliance arrives, racks, burns in, tests against your acceptance suite. Your team is in the room — knowledge transfer happens at install.
-
You run it. We back it.
Your team uses AI on your terms. We handle model updates, security patches, and performance tuning under a named-engineer SLA.
Start with step 1 →
first call · zero commitment
04 · the work behind the box
We built what we couldn't find.
Since 2023, we've worked with models from every major provider, tested hardware from prosumer to cloud-grade, and shipped AI-powered products in production. The hardest part of private inference isn't the technology — it's the logistics of assembling it into something that just works. ikioma exists to remove that barrier entirely.
FOUNDING TEAM · HELSINKI
Founded by a team with backgrounds in software development and information security. We ship AI-powered products daily — ikioma grew out of our own need for private inference that doesn't require becoming an infrastructure team.
05 · objections, answered
Things people ask before the call.
Q01 Are local models capable enough?
Modern open-weight models match cloud API performance on the categories of work most businesses care about — document understanding, summarisation, classification, structured extraction, code, agentic tool use. We benchmark against your specific workloads during the deployment call. You see the numbers before any commitment.
Q02 What about maintenance and updates?
We handle it. Model updates, software patches, performance tuning — ongoing support is included for the warranty period. A named engineer owns your account; updates are signed and reversible. You always know what changed and why.
Q03 What if our needs change?
Your system isn't locked to a single model. Swap models, scale out with additional appliances, or retune for new tasks as workloads evolve. You own the hardware; we help you get the most from it. Trade-in credit applies if you upgrade to next-gen silicon.
Q04 Is the upfront investment worth it?
Private inference is a capital asset, not a subscription line. Most customers break even versus equivalent cloud spend within 4–7 months on typical agentic workloads. On the deployment call, we'll model your specific token volume, response time targets, and compliance requirements — you'll see the crossover month for your numbers, not ours.
Your AI. / Your data. / Your infrastructure.
Every month on cloud AI is another month of variable costs, data exposure, and dependency. See what private inference looks like for your workload.
Book your deployment call →
30-minute call. No commitment. We'll model your specific workload and costs.