Sample use case
Financial services
- Workloads
- Document extraction, analyst assistants, risk investigations
- Controls
- Structured output validation · private execution · complete audit trail
Accelerator · governed execution
TerraFlow
A governed execution layer for enterprise AI and agent workloads. TerraFlow decides how an approved workload should run — context, cache, model and placement — and records why.
Enterprise AI has an execution-control problem.
Token growth is only one visible symptom. Every workload now crosses models, data, tools, policy and infrastructure.
Uncertain AI spend
Long contexts, agent loops, retries and duplicated prompts make cost per accepted outcome hard to control.
Quality varies by task
One model cannot serve extraction, reasoning, code and tool use with the same quality and latency profile.
Data cannot move freely
Sensitive data, regional requirements and internal access rules determine where a request is allowed to run.
Operations fragment at scale
Different gateways, providers, models, tool servers and GPU clusters create failure and audit gaps.
Where existing tools stop.
| Layer | What it does well | Where it stops |
|---|---|---|
| LLM gateway | Provider access, retries, basic routing | Does not understand workload outcome or private capacity. |
| Model router | Selects a model from a candidate set | Usually lacks context policy, tool control and route evidence. |
| Cache layer | Avoids repeated inference | Requires strict freshness, tenant and provenance controls. |
| Inference server | Optimises GPU serving and KV reuse | Operates below business policy and cross-provider decisions. |
LLM gateway
What it does well
Provider access, retries, basic routing
Where it stops
Does not understand workload outcome or private capacity.
Model router
What it does well
Selects a model from a candidate set
Where it stops
Usually lacks context policy, tool control and route evidence.
Cache layer
What it does well
Avoids repeated inference
Where it stops
Requires strict freshness, tenant and provenance controls.
Inference server
What it does well
Optimises GPU serving and KV reuse
Where it stops
Operates below business policy and cross-provider decisions.
TerraFlow connects these layers through one workload contract, one policy decision and one auditable execution record.
Four concepts.
Workload contract
quality, cost, latency, data
Execution choice
context, cache, model, route
Policy control
identity, residency, tools, approval
Evidence loop
outcome, audit, evaluation, learning
A router is one decision inside TerraFlow. TerraFlow owns the whole execution plan.
How TerraFlow runs a workload.
A policy-based router chooses an approved execution path for each request, within its data boundaries and token budget.
Illustrative private-data workflow. The blue route runs privately; the red gate blocks external execution for this request.
Open full diagram (opens in a new tab)01
Check policy and token limits
Identity, data residency, permitted tools and token budgets define the boundary before a request runs.
02
Choose an allowed route
Context, cache, model and placement are planned within that boundary. This sensitive request stays private; external execution is blocked.
03
Validate and record the outcome
The result is checked and a receipt records the policy, chosen route, token usage and outcome. Any fallback must also satisfy policy.
- 01
Every request starts in an application or an agent.
- 02
TerraFlow reads its workload contract: quality, cost, latency and data class.
- 03
Policy decides where it may run — identity, residency, tools, approval.
- 04
The optimiser chooses context, cache, model and route within the limits policy set.
- 05
This request runs on a private model inside your environment, because policy allows nothing else for its data.
- 06
Every execution leaves an auditable route receipt.
- 07
Outcomes feed back into the next plan.
How TerraFlow creates an execution plan.
The same lifecycle applies to a single model call, a document workflow, or each step in a long-running agent task.
- 01
Classify
identity, data class, task, service level
- 02
Constrain
allowed models, regions, tools, budget
- 03
Optimise
context, cache, route, capacity
- 04
Execute
model, tool, agent or private cluster
- 05
Validate
quality check, policy check, confidence
- 06
Learn
receipt, telemetry, evaluation feedback
If validation fails or conditions change, TerraFlow repairs the plan and escalates only when policy allows.
Optimisation extends beyond model routing.
Context selection
Bring in only the memory, retrieval results and tool outputs the task needs.
Prompt reuse
Preserve stable prefixes and use provider or private prefix caches.
Safe response reuse
Use exact and semantic caching only where freshness, access and provenance allow it.
Execution placement
Choose private, sovereign or cloud execution based on data and capacity constraints.
Adaptive routing
Match the task to an approved model, tool, specialist or escalation path.
Inference integration
Expose cache affinity, batching and self-hosted serving controls when you own the stack.
Enterprise trust is designed into the control plane.
Data and residency
Classify inputs, enforce permitted regions and execution boundaries, and protect tenant separation.
Identity and tools
Carry user and workload identity to approved tools, with least privilege and policy-scoped capabilities.
Traceability
Record the policy, route, inputs selected, cache status, retries and outcome for each execution.
Resilience and human control
Handle outages, rate limits or low confidence through guarded fallback, escalation and review.
No cache, provider, tool or fallback can bypass enterprise policy.
Sample use cases.
Sample use case
Healthcare and life sciences
- Workloads
- Clinical operations support, protocol search, regulated knowledge
- Controls
- Data boundary controls · source grounding · human review
Sample use case
Industrial operations
- Workloads
- Field-service guidance, engineering assistants, maintenance workflows
- Controls
- Low-latency deployment · tool routing · resilient fallback
Sample use case
Public sector and critical services
- Workloads
- Case-management support, citizen-service workflows, secure automation
- Controls
- Sovereign placement · access control · accountable decisions
Used in
Used in these services.
In an engagement
Policy decides where each workload may run, and every execution leaves an auditable route receipt, so the pilot is governed from its first request.
Full-stack AI solutions
AI models, apps and agents, and the integration and cloud work around them.
Explore full-stack AIData → Agentic AI
From scattered data to AI agents that do real work, on your platform.
Explore data to agentic AI
A practical path to adoption.
Start with control and visibility, then add optimisation only where it is proven to preserve the required outcome.
- 01
Establish control
- Gateway or SDK integration
- Approved model and tool registry
- Policy enforcement and route receipts
- 02
Optimise priority workloads
- Context budgets and safe cache
- Deterministic routing and fallback
- Quality validation and escalation
- 03
Scale adaptive execution
- Workload-specific learned policies
- Private-inference optimisation
- Continuous evaluation and capacity planning
Put a control layer under your AI estate.
TerraFlow gives you a durable control layer as models, tools, regulations and deployment options keep changing.