AI Control for the Lastmile.
A structured operating model that takes AI from pilot to governed production in 3–5 weeks. Methodology-led, tools-integrated, expert-led.
Built for the CIO, CFO, and CRO who own AI in production — not just the pilot.
The Problem
You can launch AI. The hard part is running it.
Most AI programs don't stall at the idea — they stall after it. The symptoms are predictable, they compound quickly, and they show up in the same three places every time.
No Path to Prod
Pilots that go nowhere
Individual use cases get built, piloted — then stall on the way to production.
- No shared platform. Every team rebuilds from scratch.
- Cost compounds. Nothing reuses.
- Scale never arrives — and the second use case looks just like the first.
Control Gap
Governance on paper only
AI policies are written, signed off, and then ignored at runtime when it actually matters.
- Risk reviews happen once, at design time — not at every prompt or release.
- Policy decisions live in slide decks, not in the gateway.
- When audit asks what the AI did, nobody can answer in under a week.
No Scoreboard
Growth without visibility
Multiple AI workflows go live with no shared signal on cost, quality, or adoption.
- Token bills appear with no allocation logic.
- Quality drifts and nobody notices until a customer complains.
- Stop-or-expand calls get made on enthusiasm, not evidence.
The Lastmile Gap
The distance between AI built — and AI operated.
Three domains need to be wired together: where AI runs, how it runs, and whether it's delivering. Most programs own only the first. That's the Lastmile — and it's where most investments stall.
- Model chosen.
- Prompt designed.
- Pipeline runs.
- Pilot shipped.
Lastmile
Where most pilots quietly stall.
- Policy enforced at runtime.
- Cost attributed to outcome.
- Value measured per use case.
- Evidence exportable on demand.
Three pillars of the operated state
Trust · Evidence · Economics
Trust
Runtime policy, guardrails, and approvals — enforced at inference, not on paper.
Every model call, retrieval step, and tool action passes through policy. Allowlists, human-in-the-loop triggers, and exception handling are versioned and audit-logged.
Evidence
Traces, evaluations, and audit trail captured for every interaction — exportable on demand.
End-to-end traces, quality scores, groundedness checks, and approval history retained per interaction. Audit packs assemble in minutes, not weeks.
Economics
Tokens and compute cost attributed per use case, team, and outcome — not guessed.
Spend mapped to workflows and users. Cost-per-interaction and cost-per-task reported automatically. Budgets enforced at the gateway, not in spreadsheets.
“Most firms deliver strategy and build. Very few operate AI in production. That's the gap we close.”
Outpace AI · Lastmile
The Operating Model
Three phases. Six disciplines. One control plane.
Each discipline stands up in a named phase — then runs continuously, with a named owner, KPIs, and a maturity target. Qualify decides what to build. Activate stands up the platform and data. Control operates with visibility and proves the value.
Reference Architecture
One control plane, above your existing model, data, and tech stack.
Lastmile is not a runtime replacement. It sits above your existing AI runtime and below your channels — standardising policy, traces, cost, and evidence across every AI service. We help you design it, tool it up, and operate it with what you already run.
Channels
Where AI shows up to your users
Consumer interfaces, internal copilots, and agent-driven workflows that actually face the business.
- Consumer AI
- Enterprise AI
- AI Workflows & Agents
Control Plane
Lastmile — sits above your runtime
Policy, traces, cost, and evidence standardised across every AI service — without replacing what you already run.
- Policy Hub
- Trace Lens
- Cost Lens
- Evidence Vault
AI Runtime
Where models, data, and tools execute
The execution layer your platform team already operates — gateways, models, retrieval, and tool actions.
- Gateway / Orchestrator
- Models & Providers
- Retrieval / Vector
- Enterprise Data
- Tools / Actions
Enterprise Ops
Where the rest of the org connects
Identity, security, incident response, and exec reporting — already in place; Lastmile feeds them, doesn't replace them.
- Identity / IAM / SSO
- Cloud Logs / SIEM
- Incident Response
- Exec Dashboards
What we help you build
- Gateway, routing, and control-layer design wired into your existing cloud
- Policy, evaluation, and observability pipelines stood up from pre-built blueprints
- Data contracts, retrieval indexes, and RAG patterns tailored to your estate
- Incident playbooks and audit export flows fit for your regulators
What we help you tool up
- Tooling selection — model providers, eval frameworks, cost observability, policy enforcement
- Integration into your cloud (Azure / AWS / GCP) without swapping your runtime
- Scorer, monitor, and guardrail configuration per use case — not generic defaults
- Runbooks and on-call wiring so platform and risk teams own operations from day one
What the control plane does
Governs · Observes · Proves
Governs AI
Every action, policy-checked at runtime.
Model calls, retrieval steps, and tool actions all pass through the policy hub before they execute. Allowlists, approvals, and exceptions are enforced — not advisory.
Observes AI
Every layer, traced and scored.
Traces, quality evaluations, and cost telemetry are captured at the source. The Trace Lens makes drift, adoption, and incidents visible in minutes — not after the fact.
Proves AI
Every interaction, evidence-ready.
Audit packs, incident records, and board-ready scorecards assemble on demand from the Evidence Vault. No manual reconstruction.
Inside the Control Plane
Four layers. One contract with the business.
Each control layer answers a different operational question — and they all run on the same telemetry. We help you build each layer, wire the right tools, and operate it in production with your platform and risk teams.
Policy Hub
Runtime guardrails, model and tool allowlists, human-in-the-loop rules, exception handling, and policy versioning — enforced at inference, not after the fact.
What we build
- Allowlist and guardrail policies per risk tier
- Approval flow wiring tied to policy outcomes
- Policy versioning, rollback, and exception paths
Tools we integrate
Lakera Guard · Guardrails AI · OpenAI Moderations · Custom policy gateways
Trace Lens
End-to-end traces, quality scoring, groundedness checks, adoption metrics, and scale/stop signal generation across every live AI service.
What we build
- OTLP trace pipeline instrumented end-to-end
- Quality, groundedness, and adoption scorers
- Drift alerts and exec dashboards per buyer
Tools we integrate
Langfuse · LangSmith · Arize Phoenix · OpenLLMetry · Grafana
Cost Lens
Token and cloud cost attribution per use case, workflow, and user group. Budget enforcement and cost-per-outcome reporting — automatic.
What we build
- Token ledger mapped to use case, BU, and owner
- Budget caps enforced at the gateway
- Cost-per-interaction and cost-per-task reporting
Tools we integrate
OpenAI Usage · Cloud billing exports · CloudWatch · Datadog · FinOps dashboards
Evidence Vault
Audit trail, trace retention, control decisions, approval history, and incident records — exportable on demand with trace, policy, and cost metadata.
What we build
- Per-interaction evidence retention and schema
- Audit export format agreed with Audit / Legal
- Incident record linkage to trace + policy metadata
Tools we integrate
S3 / GCS / Azure Blob · Parquet exports · SIEM sinks · Quarterly evidence packs
Why Lastmile
Executives feel the pain. The operating model answers all three.
CIO, CFO, and CRO each see a different symptom of the same underlying gap — between AI being built and AI being operated. Lastmile resolves it once, with one operating model, for everyone.
The Scorecard
Six KPIs. Auto-generated monthly.
One dashboard, one evidence base, one quarterly board pack. Every metric is instrumented during Activate and reported from go-live day — pulled from the tools we stand up with you, not assembled by hand.
Control Coverage
% of live AI services under policy, trace, and evidence capture.
How we measure
Inventory of live services cross-checked against Policy Hub registration, trace emission, and Evidence Vault retention.
Target: >80% of production estate
Evidence Completeness
% of interactions with retained trace + policy + cost metadata.
How we measure
Sampled from Evidence Vault exports against gateway request count; gaps flagged and triaged monthly.
Target: >95% for controlled services
Quality Performance
Groundedness, unsupported-answer rate, and task success per use case.
How we measure
Eval harness on every release + runtime scorers in Langfuse / LangSmith; thresholds agreed per use case.
Use-case-specific thresholds
Economic Efficiency
Cost per interaction, cost per task, and spend variance vs. budget.
How we measure
Cost Lens ledger joined to gateway logs and budget feeds; chargeback-ready by use case, team, and workflow.
Stable and attributable to BU
Adoption
Weekly active users, assisted-resolution rate, and absence of shadow AI.
How we measure
Trace Lens usage telemetry + gateway audit of non-registered traffic; exported to exec dashboard weekly.
Positive trend, no shadow AI
Risk Operations
Alert-to-triage time and mean time to contain AI incidents.
How we measure
Incident timestamps captured from Policy Hub + SIEM; linked to Evidence Vault records for post-incident review.
Target: hours, not days
What we build
- KPI ingestion from Policy Hub, Trace Lens, Cost Lens, and Evidence Vault
- Buyer-specific scorecard views for CIO, CFO, and CRO
- Monthly digest emails and a web scorecard owned by the sponsor
- Quarterly board-pack export with evidence attached
What we report
- Monthly: exception flags, drift signals, cost and adoption trends
- Quarterly: expand / hold / stop decision per live use case
- On-incident: assembled evidence pack with trace + policy metadata
- Board-ready PDF export with raw data extract on demand
Featured Use Case Package
Finance Copilot — grounded from day one
A source-grounded analyst assistant wrapped in policy, traces, cost, and evidence
The Finance Copilot package ships with grounded-RAG policy, a groundedness evaluator, a publish-approval workflow, per-analyst spend budgets, and a full citation audit trail — methodology-led, tools-integrated, and live in 3–5 weeks.
Faster draft cycle
Citation coverage
To go-live
Controls pre-built
Insights
Latest Thinking
Ready when you are
Ready to Outpace?
Book a 30-minute discovery call with the Lastmile team. No pitch decks, no pressure — a focused conversation on where AI can move the needle for your organisation, and whether the structured operating model is the right fit.