Reduce the cost
Models + Context
Serve compressed model weights on hardware you control, and send only the parts of a long conversation that can answer the next question.
Models ContextGoverned: compressed models and context, on hardware you own.
Every request costs less and stays inside your rules.
- Compressedmodels 1.8–2.8× smaller, and 3.2× less context sent on agent work
- Governedapproved files only, policy checked before the model answers, every decision recorded
No card to start · hosted, in your VPC, or air-gapped
Works with the clients you already run
28 have a recipe to paste, and anything else that speaks MCP works too.
SOC 2 Type II and ISO 27001: in progress
01 · Models
Most of the memory back
Every model we compress, 2–3× smaller than its base weights.
For enterprises, SaaS & AI providers →
02 · Context
Stop resending the conversation
90.7% of one long thread never resent.
For enterprises & developers →
03 · Governance
Refuse before inference
~14 tokens to decide, no answer written, every decision audited.
For governance & compliance teams →
04 · Documents
Answers from your own files
Passages cited, and never pooled with other tenants.
For support, legal & operations teams · opens the app →
Everything here is live and metered. Tell us what breaks at
hello@tileward.com.
How Tileward works
Reduce what inference has to do. Control what it is allowed to do.
AI systems resend old context, serve more model than each task needs, and leave policy enforcement to prompts. That drives up inference cost and makes control difficult to verify.
Control the work
Documents + Governance
Ground answers in approved files with citations, and refuse restricted topics before the request reaches the model.
Documents GovernanceEconomic outcomes
Lower the infrastructure cost and risk.
Tileward changes the amount of model, context, and restricted work that reaches inference.
Compute + memory
Fit more into the infrastructure you already run.
Smaller model weights and shorter prompts return memory, context capacity, and serving headroom.
Tokens + bandwidth
Move and process less repeated context.
Stop paying every agent and conversation to resend history the next question does not need.
Control + audit
Stop restricted work before it becomes output.
Make the policy decision before inference, then retain the verdict.
The larger direction
Make efficiency and control part of the inference stack.
Tileward is building the layer between AI applications and models: one place to reduce what reaches inference, define what it may use, and retain the decision record — across providers, VPCs, and air-gapped deployments.
Proof
Measure the trade-offs before you deploy.
Each result links to the workload and method behind the number.
72.1%fewer billable tokens
Across six multi-agent problems against broadcast context, with ingest included.
See the full results → 19×less context55% answer rate against 63–67% with full transcripts, judged by a separate model.
Read the full comparison → 2.2%of adversarial requests got through50 of 2,251 in our own red-team; 5 of 50 benign probes were wrongly blocked.
See the red-team results →Pricing
Start free. Pay when the workload grows.
Model usage is pay as you go, from $0.12 per million input tokens, and paid plans take up to 20% off every rate. A plan adds Context, Governance, Documents and seats around it.
Explore
$0
No card and no time limit.
Build
$39/mo
One account with all four products.
Enterprise
Custom
Your VPC or fully air-gapped.
Thirty minutes, one engineer
Bring us a long conversation, or a topic you can’t let the model discuss.
We’ll distill the first and lock the second, try to get past both, and show you the record. Thirty minutes, an engineer, no deck.
Pick a time right here on Google Calendar (no form, no waiting on a reply). Prefer email? hello@tileward.com