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Operations & Integration · From $80k

RPA and ESB out. Agent mesh in. Run cost down 50–70%.

An agent mesh reads intent rather than coordinates. It adapts to upstream schema changes without a code release, watches its own queues, and routes anything that touches a money write to a human gate.

$1.8M→$620K

yearly run cost on a mid-market bank's integration estate — same scope, agent mesh

12 wks

to replace 60 RPA bots and a full ESB estate with a reasoning agent mesh

50–70%

typical run cost reduction in the first six months after mesh cut-over

0

silent data corruptions on records of truth — the mesh catches schema drift before it writes

The core problem

Sixty RPA bots and an $1.8M ESB. The real cost is what happens when one fails.

An RPA bot clicks through a fixed sequence of screens. An upstream upgrade moves a field. The bot fails, a ticket is raised, and the quarterly release cycle fixes it. In the meantime the flow sits broken or runs manually. Multiply that across sixty bots and add an ESB estate that breaks on schema changes it doesn't control, and the maintenance cost exceeds the value delivered.

A mid-market bank ran exactly that stack at $1.8M a year. Effektiv replaced the full estate with an agent mesh in twelve weeks. New yearly run cost: $620K. APRA CPS 230 sign-off held. The bank's ops team now writes new flows in-house.

What changes

The same challenge. Two very different outcomes.

Without Effektiv

  • RPA bots breaking on every UI change
  • ESB jobs queued behind humans typing into screens
  • Long-tail exceptions papered over by manual ops teams
  • No replay harness — production traffic is the test set
  • Run cost rising 8–12% year over year
  • Vendor owns the integration map; lock-in is the model

With Effektiv

  • Reasoning agents that recover from UI drift instead of breaking
  • Agent mesh handles the long tail without the manual queue
  • Live observability board your operations team reads daily
  • Replay harness lets you test any production change against real traffic
  • Run cost down 50–70% in the first six months
  • Integration spec library, observable end-to-end, owned by your team

How we deliver

Diagnose. Design. Deliver.

Two weeks of listening before a line of code. The price is fixed at the end of Design — not at kick-off.

Phase 1 · 1–2 weeks

Diagnose

We read your bot logs, ESB queue map, and flow incident history. We map which flows touch money writes, which touch records of truth, and which are candidates for full automation. Silent failures — flows that complete without error but write corrupted data — are identified here.

Phase 2 · 1–2 weeks

Design

The mesh architecture, human-gate rules, and phase-out plan for the old stack. Any flow touching a financial record or a record of truth carries an explicit human approval gate. APRA CPS 230, ASIC RG 255, and equivalent frameworks mapped here. Price fixed at end of Design.

Phase 3 · 8–14 weeks

Deliver

The new mesh runs in parallel alongside the old stack until the evaluation period passes. The switch-over is clean and tested. Runbooks, eval rules, and mesh configuration are yours at exit — so the ops team can extend the mesh without calling us back.

What you walk away with

Everything ships to your team at exit. No lock-in.

🛠

Agent mesh in production

RPA and ESB displaced by reasoning agents that handle the long tail. Observable end-to-end from day one.

🧪

Integration spec library

Every external system mapped, contract-tested, observable. Owned by your team at exit.

🗄

Live observability board

Latency, error rate, queue depth, contract test pass rate, message-loss tolerance.

📒

Eval rig source code

Five operations gates as runnable code in your repository. Yours forever.

🎓

Replay harness

Replay any production traffic against a candidate change before it merges.

Quality gates

What the eval rig measures.

Every output passes a multi-gate evaluation before it merges or ships. Outputs that fail do not proceed. The eval rig and all gate code are yours at exit.

  • Integration latency under SLO per endpoint — budgets agreed in Design
  • Data corruption rate at zero tolerance on records of truth
  • Queue depth under sustained load, with back-pressure handling proven before cut-over
  • Contract test pass rate across every external system the mesh talks to
  • Message-loss tolerance under simulated upstream outage

Eval rig · sample run

Integration latency under SLO per endpoint — budPASS
Data corruption rate at zero tolerance on recordPASS
Queue depth under sustained loadPASS
Contract test pass rate across every external syPASS
Message-loss tolerance under simulated upstream PASS

Eval rig source code shipped to your repo at exit.

Sample engagement

A mid-market bank ran 60 RPA bots and an ESB estate at $1.8M a year. Bots fell over each quarter as upstream systems changed. Effektiv replaced the full estate with an agent mesh over 12 weeks. New run cost: $620K. APRA CPS 230 sign-off held. The mesh now catches upstream schema changes before they corrupt the record of truth. The bank's ops team writes new flows in-house.

Read the full case →

Compliance posture

ISO 27001 in progress (Q3 2026) ISO 42001 aligned NIST AI RMF mapped IRAP path Q4 2026 Full governance posture →

Other services

Other ways we work with you.

Common questions

Frequently asked questions.

Outcome-priced against your current run cost

See what your integration estate costs to run with an agent mesh.

Show us your current bot count, ESB estate, or ops team cost. We price the mesh on outcomes — your run cost reduction is the benchmark, not our hours.