Travel and Mobility · Operations & Throughput
Automate Procurement in Airports with AI
For airport operators, passenger experience teams, commercial directors, and ground operations leaders ready to move procurement automation from manual operation to instrumented AI-native delivery. Below: the workflow we ship, the operating model that keeps it improving, the governance posture, and the commercial envelope.
Projects from $15k · Refundable 7 days · Kickoff within 5 days
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
In one sentence
AI-native procurement automation for airports — Production procurement automation for airports delivered in vertical slices, each gated by the labelled test set captured during Discovery, each handing operational ownership progressively to your team. Expected delta on cycle time: −81%.
Key facts
- Industry
- Airports
- Use case
- Procurement Automation
- Intent cluster
- Operations & Throughput
- Primary KPI
- cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction
- Top benchmark
- Rework / case: 21% → 4% (−81%)
- Systems integrated
- AODB, FIDS, baggage systems
- Buyer
- airport operators, passenger experience teams, commercial directors, and ground operations leaders
- Risk lens
- security, passenger safety, airline coordination, and operational resilience
- Engagement timeline
- Discovery 2.5 weeks → Build 7 weeks → Run continuous
- Team size
- 2 senior delivery (1 architect + 1 implementer)
- Discovery price
- $6k · 2-week sprint
- Build price
- $20k–$28k · 6-10 weeks
Primary outcome
buy faster while improving supplier discipline
What we ship
supplier research assistant, intake workflow, RFP copilot, and contract handoff
KPIs we report on
cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction
Why Airports teams hire us for this
What separates AI-native procurement automation from "AI features added on top" is operating discipline. The pattern that works in airports is the same one that works for any high-stakes operational system: instrument the baseline, ship a thin slice to production, govern explicitly, then expand. We run every engagement against that pattern.
Operations benchmarks across airports typically show 20-35% of operator time absorbed by status checks, handoffs, and exception triage. AI-native automation reclaims that block first because it has the highest volume and lowest decision risk.
Industry context: Airports coordinate 30+ stakeholders per flight (airlines, ground handlers, security, retail, customs). Passenger flow metrics drive concession revenue (every minute saved at security adds ~$0.40 / pax retail spend per ACI benchmarks).
Benchmarks we hit
Reference benchmarks from production deployments of procurement automation in airports-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Rework / case Includes manual re-entry, customer call-backs, and reviewer escalations | 21% | 4% | −81% |
Cost per transaction (fully loaded) Includes AI inference cost, reviewer time, and infra amortization | $14.20 | $3.85 | −73% |
Time-to-onboard new operator AI assistant handles the long tail of edge cases that previously required senior coaching | 8 weeks | 2 weeks | −75% |
Benchmarks are reference values from comparable engagements and authoritative sector benchmarks. Your engagement's baseline is captured during Discovery and actuals are reported weekly during Run against that baseline.
How we operate the workflow
Our operating model is borrowed from production engineering, not consulting. Every prompt has a version. Every output has a confidence score. Every decision has a reviewer or a logged rule. The result for procurement automation is a workflow that Airports leaders can defend in front of a CFO, a risk officer, or an auditor — not a demo that impresses once.
What we build inside the workflow
A strong implementation starts with a clear inventory of the current work. For Airports, that means understanding how data moves through AODB, FIDS, baggage systems, retail analytics, security operations platforms, who owns each decision, and where handoffs slow the team down. We document current cycle time, error rates, quality review steps, rework, and the volume of requests or records flowing through the process. The automation layer will summarizes supplier options, drafts RFPs, checks policy fit, compares bids, and tracks approvals.
Reference architecture
4-layer AI-native workflow for operations & throughput
Four layers, in the order data flows through them: intake (classify and tag), context (retrieve approved sources), action (draft, route, decide), review (humans on low-confidence and high-impact cases). Each layer is independently observable.See the full architecture diagram for Operations & Throughput →
AI-native vs traditional approach
Side-by-side comparison of an AI-native engagement against the alternatives most airports teams evaluate for procurement automation: time to production, pricing model, governance posture, operator throughput, unit cost, exit path.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Time to production | Two quarters minimum | Production traffic within 6-10 weeks |
| Pricing model | FTE hourly retainer or fixed staffing | Three independent commercial envelopes |
| Audit / governance | Document-driven, periodic snapshot | Runtime guardrails + audit log + governance map + quarterly attestation |
| Operator throughput lift | 1.0× (baseline) | −73% |
| Cost per unit | Linear with operator headcount | Typically 60-80% lower |
| End-of-engagement | Multi-quarter notice + knowledge loss | Month-to-month Run, full handover plan in Build SoW |
Manual gate coordination costs 4-7 FTE per terminal; AI-native orchestration brings the same coverage to 1-2 FTE with audit-ready logs for IATA Slot Conference disputes.
Engagement scope & pricing
Procurement Automation delivery is structured as Discovery → Build → opt-in Run, each priced and scoped independently. No multi-quarter retainer commitments.
Operations engagement
Three commercial envelopes, three deliverables. The next phase is scoped against the evidence the prior phase produced.
Phase 1 · Discovery
$6k
2-week sprint
Phase 2 · Build
$20k–$28k
6-10 weeks
Phase 3 · Run
$2.5k–$4k / mo
optional, hourly bank also available
~$32k–$58k typical year 1 (60% take the run option for ~6 months)
Workflow redesign, system integration, governance, and weekly operating cadence during Run.
Start with Discovery; nothing more is required to begin. Build is scoped from the Discovery output. Run, if it happens, is month-to-month with no lock-in.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
We sit with the operator team running the workflow today, watch a working day end-to-end, and produce the baseline that Build will be measured against. Two-week sprint, fixed price.
Phase 2 · Weeks 2–4
Design
Architecture sprint covering the four-layer workflow (intake, context, action, review), the integration footprint, the evaluation methodology, the reviewer UX, and the governance map.
Phase 3 · Weeks 4–8
Build
Vertical-slice delivery against the labelled test set. Each slice ships to production, gated by eval criteria. By end of Build, the workflow is operating on real traffic with the calibration discipline established.
Phase 4 · Weeks 8+
Run
We run the workflow with you weekly, expand into adjacent work, and report against baseline.
Interactive ROI calculator
Estimate your AI-native ROI for procurement automation
Reference inputs below are typical for airports teams in the operations cluster. Adjust them to match your situation.
Projected
Current monthly cost
$56,000
AI-native monthly cost
$18,520
Annual savings
$449,760
67% cost reduction · ~2,601 operator-hours freed / month
Governance and risk controls
We map every airports engagement against the NIST AI RMF functions (Govern, Map, Measure, Manage) during Discovery. The risk register we produce covers security, passenger safety, airline coordination, and operational resilience, and it drives the design choices in Build: which decisions get full automation, which get assisted review, which require explicit human approval. The map is a living artefact reviewed quarterly during Run.
How we report ROI
We refuse to project ROI before Discovery. The honest answer for most airports engagements is: we will compress the cycle for buy faster while improving supplier discipline by 30-70%, lift consistency on cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction, and reduce reviewer load on the routine cases — but the magnitude depends on the baseline we measure together. The Discovery report contains the projection.
Selected portfolio
Real builds — procurement automation in airports and adjacent sectors
Below are engagements drawn from our active portfolio where the workflow rhymed with procurement automation in airports or in adjacent contexts. Scope and stack are accurate; client identities are withheld under engagement NDAs.
Q3 2025
On-demand regional aviation booking — flexible flight network across smaller cities
Regional aviation operator · DACH
Booking and operations stack for an on-demand regional aviation network connecting secondary cities. Customer-facing booking flow with dynamic availability, operator-side dispatch tools, route economics dashboards. Designed for a sustainable flight-network operating model rather than fixed-schedule airline patterns.
- Next.js + native-app companion
- Dynamic availability engine
- Operator dispatch console
Q3 2025
Radiology workflow application — case handling and reporting
Medical imaging operator · Europe
Application supporting radiology workflow: case intake, structured reporting, document handling, and quality-assurance loop. Designed for regulated medical-imaging context with audit trail and role-based access.
- Web app + secure storage
- Structured reporting
- Audit-trail compliance
Q4 2025 → Q1 2026
Owners-association management SaaS — 55+ screens, 47 normalized tables
Mid-market property operator · GCC region
Full operational backbone for a property operator running multiple owners associations: properties, units, owners, accounting, service charges, budgets, maintenance, violations, and a resident-facing community portal — replacing a patchwork of spreadsheets and disconnected accounting tools.
- Next.js + tRPC
- PostgreSQL · Drizzle ORM
- JWT federated identity
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.
Common pitfall & mitigation
The failure mode we see most often on AI-native procurement automation engagements in airports contexts.
Edge cases break the prod thin slice
AI handles 80% but the 20% long tail still floods the human queue
Discovery captures the edge-case taxonomy; Build allocates 30% of effort to the edge-case router
Week-by-week shape of the Build phase
Our Build cadence on procurement automation for airports is bias-corrected against the two failure modes we have seen kill airports AI projects most often: scoping that drifts week-by-week, and a labelled test set that arrives in week 6 instead of week 1.
We fix the scoping by signing the Build statement of work before any code is written — the deliverables are named, the integration footprint is bounded, the milestones have dates. We fix the labelled test set timing by treating it as the week-1 deliverable. Week 1 is not "scoping week" — it is "labelled-test-set week", because every subsequent engineering decision is measured against that test set.
Week 2: retrieval index live with first batch of approved sources. Week 3: intake classifier scoring against the test set, first calibration report. Week 4: action layer drafting with reviewer approval; first end-to-end case flow. Week 5-6: thin slice in production on 5-15% of routine airports traffic, first weekly review with the operator team. Weeks 7-10: production envelope widens case-class by case-class, calibration loop tunes against the empirical evidence, exceptional cases route to enriched escalation. By day 60-70, the workflow is operating at its target envelope.
Build internally or work with us
Airports teams that build successfully in-house tend to have an existing ML platform, a labelled data culture, and a product manager dedicated to the workflow. If any of those is missing, the project tends to stall at proof-of-concept. We replace those three dependencies with a scoped engagement and a senior delivery team.
What to ask us before signing
- Ask for a workflow map that shows intake, retrieval, generation, review, escalation, system updates, and measurement.
- Ask for an evaluation plan using real examples from airports, not only generic test prompts.
- Ask how we will move cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction within the first 30 to 60 days.
- Ask which parts of the process remain human-owned and why.
- Ask for our exit plan: what stays with you if the engagement ends.
Recommended first project
Pick the procurement automation flow that has three properties: high enough weekly volume to produce a labelled test set quickly, structured enough to evaluate, and reversible if a decision is wrong. That is the wedge that ships fast, proves adoption, and earns the credibility to extend into the harder cases. The first 30 days are spent on the labelled test set, the integration to AODB, and the thin-slice workflow. The next 60 days are spent operating the thin slice on real airports traffic, widening the automation envelope week by week. By day 90 you have an empirical track record, not a vendor's projection, and the next workflow can be scoped against that evidence.
Frequently asked questions
How do you automate procurement automation in airports with AI?+
Three phases. Discovery (2 weeks) produces the labelled test set, the system map, and the Build statement of work. Build (6-10 weeks) ships a thin-slice production deployment on top of AODB and adjacent systems, with versioned prompts and a reviewer queue. Run (optional, month-to-month) operates the workflow weekly against cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction.
What does it cost to automate procurement automation for airports teams?+
Three phases, billed separately. Discovery sprint: $6k (2-week sprint). Build engagement: $20k–$28k (6-10 weeks). Run retainer: $2.5k–$4k / mo (optional, hourly bank also available). ~$32k–$58k typical year 1 (60% take the run option for ~6 months). Workflow redesign, system integration, governance, and weekly operating cadence during Run.
What is the best AI agent for procurement automation in airports?+
There is no single "best" off-the-shelf agent for procurement automation in airports — the right architecture depends on your AODB setup, your data, and your risk profile. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for AODB and FIDS integrations, and a reviewer queue. We benchmark candidate models against a labelled test set during Discovery and pick the one with the best accuracy/cost ratio for your workflow.
How long does it take to deploy AI procurement automation for airports?+
End-to-end lead time from kickoff to thin-slice production: 6-10 weeks. End-to-end to full operating envelope: 10-14 weeks. cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction is instrumented from day one of Build; the dashboard goes live by week 4-5; production traffic starts by week 6-8. By 90 days, leadership has a 30-60 day record of operating performance against the Discovery baseline.
What do we own, and what do you own?+
We own the workflow design, the prompts, the retrieval architecture, the evaluation harness, and weekly improvement. Your airport operators, passenger experience teams, commercial directors, and ground operations leaders team owns data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
What's the operating cadence during Run?+
Monday metric review, Wednesday prompt and retrieval refresh, Friday calibration audit. The cadence is the deliverable; the prompts are the artefacts that change between cycles. Quarterly architecture retrospective. The cadence is documented and absorbable by your operator team progressively during the first quarter of Run.
Do you train models on our data?+
No. We do not train any model on client data. Anthropic Zero-Data-Retention is enabled by default; OpenAI default-no-training is honoured. Prompts, retrieval indexes, audit logs, and integration data live in your cloud account under your IAM. At engagement end, every artefact transfers to your repository.
What if we want to exit the engagement?+
Discovery and Build are fixed-scope, so there is no mid-engagement exit cost. Run is month-to-month with 30-day notice. Every artefact (prompts, eval harness, integration code, dashboards, runbooks) is in your repository throughout the engagement, not behind our SaaS. There is no lock-in.
What does success look like 90 days after Build closes?+
cycle time, savings, supplier risk, contract leakage, and stakeholder satisfaction measurably improved against the Discovery baseline. Your team is operating the workflow with the cadence we shipped during Build. The audit log is queryable. The reviewer queue is calibrated. The next workflow scope is informed by real production evidence rather than initial assumptions.
What support is included after the engagement ends?+
Optional Run retainer covers weekly cadence, prompt refresh, retrieval index updates, and reviewer-queue calibration. Architecture-level questions and breaking-change support are billed hourly outside of Run. Most engagements transition Run in-house at month 6-12; we stay available for architecture decisions for 12 months at no extra charge.
How does this integrate with AODB and our existing stack?+
Discovery scopes the integration footprint explicitly. We integrate at the API layer; no replatforming required. The Build statement of work names exactly which systems are connected, which data flows are bidirectional, and what authentication patterns we use (SSO, service accounts, OAuth scopes). The integration code lives in your repository.
What does your team look like during an engagement?+
Discovery: 1 senior delivery lead + 1 PM, ~30 hours/week. Build: 1 senior delivery lead + 2-3 senior AI engineers, ~50-80 hours/week across the team. Run: 1 delivery owner + 1 engineer on weekly cadence. We do not use offshore staff augmentation. Every engineer touching your engagement is senior-level.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on airports engagements. Cited here so you can verify and dig deeper.
- ACI World Airport IT
- Helpful, reliable, people-first content — Google Search Central
- Responsible Scaling Policy — Anthropic
- Future of Work: Operations — Deloitte Insights
- Lighthouse Network — Operations AI Adoption — World Economic Forum + McKinsey
- ICAO Innovation — International Civil Aviation Organization
- Google Search Central: URL structure best practices
Concepts on this page:
AI workflow·Thin slice·Reviewer queue·Evaluation harness·Tool use·Audit logFull glossary →High-intent reads
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Start a Airports engagement
Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.