StradaverseStrada AI+ · StradaFDE

Chaos

Systems that do not talk. Knowledge that lives in people. Decisions made late.

into order

Firm Sovereign Intelligence: one core, every function connected.

and superboost.

Agents on every floor. The same people, many times the reach.

Built with StradaFDE.

Forward deployed engineers, inside the business, floor by floor.

Strada AI+ · Enter the era of AI+

Enterprise AI is moving beyond experimentation. Businesses need AI that understands their operations.

ProgrammeStrada AI+, the enterprise AI programme of Stradaverse
FoundationFirm Sovereign Intelligence: a firm's knowledge, embedded and owned
EcosystemStradaFDE, forward deployed engineers and specialists
EventSaturday 24 October 2026

01 · Eight open problems. One building.

01 · Eight open problems, in detail

None of these is a finished product. Each is a floor of the same building.

Each carries two questions. The open question is the part we want help with. The FDE question is where a forward deployed engineer starts: inside the current process, freeing capacity in that function so the same people move to the next one.

Floor 01 · Finance

From chasing payments to intelligent collections.

Credit Control Agents. Receivables teams chase overdue invoices from spreadsheets and memory. Which account to call first, what to say and when to escalate is decided late, under pressure and inconsistently.

What we want to build. Agents sit on the receivables ledger, read aging, payment history and customer context, score risk and prepare prioritised actions with drafted communications. A credit controller reviews and sends. The result is earlier, more consistent follow-up and a clearer view of cash at risk.

  • Receivables monitoring and aging analysis
  • Payment-risk prediction from payment patterns
  • Drafted reminders prepared for review
  • Collections prioritisation by value, risk and relationship
  • Escalation cases assembled with full history
The open questions

Can credit control run as a fully automated closed loop: invoice issued, aging watched, reminder sent, call list built, escalation raised, payment matched, loop closed, with a person only at the escalation step?

What share of overdue invoices can be collected with no human touch, and at exactly which point does the agent hand over?

Where an FDE startsSit with the credit team through one full chase cycle. Map every step from aging report to call to escalation, automate the first two, and measure the hours that come back. Those hours are the first capacity that moves to the next function.

In build nowHuman oversight. Sensitive communications, disputes and escalations are prepared by agents and decided by people. No agent sends a demand or changes credit terms on its own.

Floor 02 · Finance

Finance that thinks ahead.

AI Finance Workforce. Month-end is a relay of reconciliations, matching, exception hunting and report assembly. Skilled people spend most of their time moving data between systems rather than analysing it.

What we want to build. A set of specialised agents, each bound to one task and one system, works alongside the finance team: reconciling, matching, flagging exceptions, drafting variance commentary and preparing audit packs. Finance reviews, decides and signs. The close gets faster and more reliable, and the work turns forward-looking.

  • Bank and ledger reconciliation with explained breaks
  • Accounts payable capture, matching and coding suggestions
  • Reporting drafts and variance commentary
  • Cash-flow forecasting scenarios
  • Exception detection across journals and invoices
  • Audit evidence gathering
The open questions

Which parts of month-end can agents close end to end: bank reconciliation, AP matching, intercompany, accruals? What percentage of journals need no human touch?

How many days can the close shrink, from first cut to signed, and what does the controller still have to sign?

Where an FDE startsShadow one month-end. Time each reconciliation and handoff, find the three that eat the most days, and put an agent on each with the controller signing. The freed days become the finance team's capacity for forecasting, not more closing.

Under explorationHuman oversight. Agents support finance teams and hold no approval or payment authority. Figures are computed by deterministic systems, never by a language model.

Floor 03 · Manufacturing

From production data to production intelligence.

Intelligent Manufacturing. Machines, MES and quality systems each hold part of the story. Downtime is explained after the fact, bottlenecks move faster than the weekly review, and yield drifts before anyone notices.

What we want to build. A production intelligence layer reads machine signals, orders and quality data together. Agents watch for drift, suggest maintenance windows, explain downtime and support planning decisions. Fewer surprises on the floor, earlier maintenance, faster root cause.

  • Production monitoring with live-style status
  • Predictive maintenance from condition trends
  • Quality inspection support and defect patterns
  • Production planning and sequencing assistance
  • Bottleneck and downtime analysis
  • Yield recommendations
The open questions

How many hours of unplanned downtime can be converted into planned maintenance windows, and how far ahead can the signal be trusted: hours or days?

What percentage of defects can be caught at the station instead of at final QA?

Where an FDE startsWalk the line with the planner for a week. Find where downtime is explained by hand and where the weekly review is already too late, wire the signals, and give the planner back the hours spent reconstructing yesterday.

Under explorationHuman oversight. Recommendations go to planners and maintenance leads. The drawing runs on simulated data.

Floor 04 · Warehouse

The warehouse that can see, simulate and optimize.

3D Warehouse Intelligence. Warehouse decisions are spatial, but the data lives in lists. Slotting, picking routes and space use are tuned by instinct, and the effect of a change is only visible after it is made.

What we want to build. A 3D digital twin of the layout carries operational data: pick frequency, traffic heat, utilisation. Scenarios are simulated on the model before anyone moves a pallet. In the drawing, run the next pick, then re-slot the fast movers and compare the routes. Drag to look around.

  • Rack, slot and zone modelling
  • Heatmaps of pick frequency and traffic
  • Slotting recommendations
  • Picking route simulation and re-sequencing
  • Space utilisation analysis
  • Forklift and robot movement
The open questions

How many percent of efficiency can AI putaway, AI slotting and AI picking each add, measured in picker travel metres, picks per hour and dock-to-stock time?

Can slotting be re-optimised every night instead of once a year, and how much route length does that remove?

Where an FDE startsRide along with pickers for a shift. Measure the real routes, model the layout, and let the supervisor test a re-slot on the twin before anyone moves a pallet. Shorter routes free picker hours for inbound and quality.

Under explorationSimulated, not measured. The route numbers come from this simulation and illustrate the method. They are not results from a customer site.

Floor 05 · Customer

From customer records to customer intelligence.

AI CRM. CRM systems record what happened but rarely say what to do next. Follow-ups slip, signals in emails and calls go unread, and pipeline reviews run on gut feel.

What we want to build. An intelligence layer over interactions, opportunities and service history. Agents qualify leads, read sentiment, surface pipeline risk and recommend the next best action for the account owner, who decides what is sent and promised.

  • Lead qualification and enrichment
  • Opportunity and pipeline insights
  • Drafted follow-ups for review
  • Sentiment across channels
  • Next-best-action recommendations
  • Service agents with escalation to people
The open questions

What percentage of inbound email, calls and tickets can be captured, classified and logged with no manual entry?

Can the next best action be ranked well enough that account owners follow it more often than not, and does the pipeline hit rate move?

Where an FDE startsSit in the sales huddle and the service queue. Count the follow-ups that slip and the admin that fills the day, automate the capture and the drafts, and hand the account owners their selling time back.

Under explorationHuman oversight. Agents recommend and draft; account owners decide. Customer data stays inside the firm's governed environment.

Floor 06 · People

People operations, reimagined.

AI HR. HR teams answer the same questions hundreds of times, run onboarding by checklist and email, and plan the workforce from spreadsheets that are out of date on arrival.

What we want to build. HR agents sit on the system of record and its policies: onboarding flows, a knowledge assistant that cites policy, employee service workflows and administrative automation. Faster answers for employees, and HR time redirected from administration to people.

  • Onboarding orchestration
  • HR knowledge assistance with policy citations
  • Guided requests and policy support
  • Workforce planning support
  • Training recommendations
  • Administrative automation
The open questions

What percentage of employee questions can be answered from policy, with a citation, and zero HR touch?

Can onboarding run as a closed loop from offer accepted to day-one ready (accounts, equipment, training, compliance), with HR approving only the exceptions?

Where an FDE startsLog a week of HR questions and onboarding steps. Answer the repeated ones from policy with citations, orchestrate the checklist, and move the HR partner's time from administration to the people conversations only they can have.

In build nowSafeguards built in. Privacy by design, fairness checks, transparency about when AI is involved, and human decisions for hiring, pay, performance, discipline and termination. AI never makes consequential employment decisions on its own.

Floor 07 · Workforce

Understand work. Unlock capacity.

Workforce Intelligence. Leaders cannot see where capacity is, which skills are scarce, or which workstreams are overloaded until delivery slips. Staffing is decided by who is remembered, not who is right.

What we want to build. A workforce graph of teams, skills, workstreams and agent capabilities. Agents map skills, flag gaps, support staffing and show where human-and-AI collaboration changes the shape of work. Capacity and skills become visible early enough to act.

  • Capacity planning
  • Skills mapping and gap analysis
  • Staffing and allocation support
  • Workload visibility by team and workstream
  • Learning recommendations
  • Human-and-AI collaboration planning
The open questions

Can the capacity each FDE engagement frees be measured in hours per team per week and shown on one view?

Which skills gap blocks the next function, and how long until the same team can take it on?

Where an FDE startsThis is the problem that makes the others compound. Every hour an FDE frees in one function has to be visible, so it can be moved to the next one on purpose, team by team, and the business grows sideways without growing headcount.

Under explorationBy design. No individual productivity scoring and no invasive monitoring. The unit of analysis is the team and the workstream, and people can see what the system knows about them.

Floor 08 · Enterprise systems

One enterprise. Many intelligent agents.

Enterprise Agent Orchestration. Each department automates its own corner. The process that matters, order to cash or production to delivery, crosses six systems and still runs on handoffs, emails and waiting.

What we want to build. Specialised agents, each bound to one system and its rules, coordinated by an orchestration layer that carries context across the process and brings people in at the points that need them. The drawing follows one order end to end.

  • Order intake and validation
  • Inventory check and allocation
  • Production planning request
  • Finance validation of credit and margin
  • Fulfilment coordination
  • Proactive customer updates
The open questions

Can one order run from CRM to customer update with zero re-keying across six systems, and how many human touches does it take today versus after?

What percentage of orders can complete with no human step except the credit and margin approval?

Where an FDE startsFollow one order end to end with the people who touch it. Mark every wait and every re-key between systems, then remove them in sequence. The time that comes back is spread across six functions at once.

Under explorationFirm Sovereign Intelligence. The orchestrator inherits each user's authorisation and the firm's rules; it never reaches data a person could not. Approvals stay human at the steps the firm defines.

02 · The foundation

Firm Intelligence is a foundation, not a feature.

Under every floor of the building sit the same five layers: the systems of record, the firm's captured knowledge, an agent runtime bound to those systems and their rules, governance with human gates, and the experiences people use. Build the foundation once and every problem above inherits it.

03 · The builders

StradaFDE. Forward deployed engineers for Firm Intelligence.

Enterprise AI requires more than models. It requires engineers who can connect systems, design agents, build interfaces, orchestrate workflows and turn complex business problems into working solutions. FDEs build inside the business, next to the work that changes.

  1. i.

    Go into the current process

    Sit with the team that runs it today. Real process, real data, real constraints, with the people whose work changes in the room.

  2. ii.

    Find what can be improved

    Map the waits, the re-keying and the hand-work. Put agents on the parts that are judgement-free; keep a person accountable for the parts that are not.

  3. iii.

    Measure the hours that come back

    Efficiency is counted in people-time returned to the function, not in features shipped.

  4. iv.

    Take that capacity to the next function

    The same people grow into the next floor of the building. The business grows horizontally without growing headcount, and the FDE moves with it.

What it is
A network of builders Strada AI+ brings onto real enterprise problems
What you get
Real problems, the Firm Intelligence foundation to build on, and a network that knows your work
What it is not
A job board. Joining promises no assignment; it puts you in the room when the right build appears
Agents and models
  • Agentic AI
  • LLM engineering
  • Retrieval-augmented generation
  • Observability and evaluation
Systems and data
  • Enterprise integrations
  • Data engineering
  • Workflow automation
  • Azure AI and cloud architecture
Interfaces, space and trust
  • Full-stack development
  • 3D visualisation
  • Spatial computing
  • Digital twins
  • AI security and governance

04 · Horizon

What we build next starts with who we connect today.

The honest shape of the pipeline: what is in build now, what is being explored, and where the ambition points for 2027. No client names and no signed-project claims.

In build

Now

  • Credit control agents
  • AI HR
  • AI TMS
What Strada AI+ is building now. Client details are not disclosed.
Under exploration

Next

  • AI Finance
  • AI CRM
  • AI Manufacturing
  • AI Warehouse
  • AI Operations
  • Workforce intelligence
  • Enterprise agent orchestration
  • Spatial and digital-twin interfaces
  • Sovereign deployment patterns
Solution areas being scoped with prospective enterprises. Not commitments.
Direction

2027

  • Agent networks across finance, operations and workforce
  • Intelligence assets owned and governed by the firm
  • A developer ecosystem shipping against real enterprise opportunities
Direction, not a pipeline.

05 · The invitation

Great enterprise AI is built together.

StradaFDE is our growing network of developers, engineers and technical specialists interested in shaping practical enterprise AI. Joining is an invitation to connect, not an application, and it promises no assignment.

Join StradaFDEYour next big build may start with one connection.

06 · Join the developer pool

Builders, this is your signal.

Register your interest to stay connected with upcoming enterprise AI initiatives, developer collaborations and opportunities across the Strada ecosystem.

  • Three fields are enough. Add more if you want us to know what you build.
  • We contact you when something relevant emerges: a build, a prototype, a workshop, a conversation.
  • Your details stay with the Stradaverse team. No marketing unless you ask for it.

Saturday 24 October 2026.
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