The layer that takes AI from pilot to production.
Supervisor agents plan, specialised agents act inside your core systems; every step is traced, passes human approval where needed, and is costed. Model-agnostic; on-prem, hybrid or cloud.
The agents below are simulations derived from real agent definitions. Pick a sector to watch a different scenario.
Dialogue banking: voice and text transaction assistant
- App chat: "pay my electricity bill, raise my limit to 40k, I don't recognise yesterday's 1,250"
- Three intents separated: bill payment · limit increase · transaction dispute; order and dependencies set
Simulation · derived from real agent definitions · every agent can be built by dialogue with the Autonomous Agent and validated with a test corpus
The hard part of enterprise AI is getting to production.
Most enterprises are trying something with AI; very few can run it in production, at scale, under control.
The PoC gap
Pilots impress, but the path to production is undefined.
Scattered initiatives
Every unit trials its own tool; no enterprise coherence.
Unmeasured value
The gap between “it works” and “it pays off” goes unreported.
Governance gap
Security, compliance and auditability are afterthoughts.
Outsourcing dependency
Person-months are bought; knowledge leaves with the person, timesheets do not show what was done.
Result: budget spent, transformation missing.
From consulting to platform, from infrastructure to operations.
A partner that owns enterprise AI transformation and runs it end-to-end. Four components under one roof; accountability in one place.
Expert team
Strategy, data, engineering and change management in one experienced team. Consultants do not come and go; we operate the product with you.
The Aginies platform
An enterprise-grade agentic orchestration layer that runs every AI workload from one place. Every AI scenario in the organisation runs on the same execution layer, acts inside core systems and leaves an audit trail.
Sovereign infrastructure
Independent operation within national borders with GPU-as-a-Service. Data and model control stay with you; usage-based capacity with no capital outlay.
Agent workforce
Outsourced agents instead of outsourced people: roles such as iOS developer, business analyst, accountant and tester work in your tools; every task in the work log, decisions with you.
Every agent starts as a dialogue.
Describe what you need; the Autonomous Agent sets up the supervisor and specialist agents, binds tools and the knowledge base, runs the test corpus and deploys with your approval. Every agent in the library can be produced this way.
Autonomous Agent and the product family →The agent will appear here
- Platform AginiesEverything comes together hereWhat is includedSee the screen and details →
- Visual agent builder, workspace and tenant management, roles via SSO
- Execution engine, approval cards, kill-switch, dashboards and reports
- Knowledge-base management and the Autonomous Agent
With the organisation’s identity provider (SAML/OIDC), SIEM, data warehouse and ticketing.
77 / 77 scenarios use it - Voice AginiesSTT · TTS · ASRWhat is includedSee the screen and details →
- Streaming Turkish ASR and STT (aginies-voice-tr-v3), natural TTS (tts-tr-v2)
- Voiceprint verification, spoken OTP, warm hand-off
- Call quality analysis: transcript, sentiment, compliance checks
SIP with Genesys, Avaya and open-source switches; API with recording systems.
23 / 77 scenarios use it - Code AginiesIDE Registry · IDE extensions · code completionWhat is includedSee the screen and details →
- IDE extensions (VS Code, JetBrains) and in-house code completion
- IDE Registry: approved extension and model distribution, version pinning
- SDLC agent team: analysis, design, development, test, review
With Jira, Git (GitHub, GitLab, Bitbucket, self-hosted), CI/CD and static analysis tools.
8 / 77 scenarios use it - Hub AginiesModel Hub · MCP Hub · Model proxyWhat is includedSee the screen and details →
- Model catalogue: tulpar, talay, sunkar, konrul families and current open releases
- Model proxy and key management; usage and cost tracking
- MCP Hub: approved tool servers, invocation policies, connection tests
With in-country GPU pools; with the organisation’s chosen cloud model providers through the proxy.
77 / 77 scenarios use it - Vision AginiesDocuments · images · maskingWhat is includedSee the screen and details →
- Invoice, contract, form and ID document extraction; per-field confidence
- Damage-photo assessment and visual quality control
- Personal-data detection and document masking
With document stores (S3, SharePoint), scanners and ECM; results written to ERP and claims systems through tools.
21 / 77 scenarios use it - Workforce AginiesAgent workforce, on demandWhat is includedSee the screen and details →
- Role catalogue: engineering, business analysis, finance, support, HR, legal
- Work log: task, output, hour-equivalent, quality score, review status
- Shadow period → sampled review → autonomous; a named team lead
With the organisation’s own tools: Jira, Git, ERP, ticketing, e-mail.
17 / 77 scenarios use it - Dialog AginiesDialogue UIWhat is includedSee the screen and details →
- Web and mobile dialogue UI; embeddable widget
- WhatsApp, SMS and e-mail channels with the same agent
- Approval cards, file and image sharing, warm hand-off
With the organisation’s identity and OTP stack; with the WhatsApp Business API and SMS gateways.
60 / 77 scenarios use it
Hire agents, not headcount.
Instead of hiring an iOS developer, business analyst, accountant or tester from an outsourcing firm, you hire agents that hold the same role. The agent works in your tools and reports to your team; what it did, how long it took and what it cost are in the work log.
Role catalogue and engagement model →- iOS Developer Agentreplaces: Outsourced iOS developerPRs / week · First-review pass rate
- Business Analyst Agentreplaces: Outsourced business analystStories / week · Return rate from development
- Accountant Agentreplaces: Outsourced accountantDays to close · Reconciliation breaks
- QA Engineer Agentreplaces: Outsourced testerEscaped defects / release · Bug validity rate
Track agent activity, platform health and model costs from one dashboard.
Every run is recorded step by step; every request can be masked and anonymised. Separated by workspace and tenant; teams share and co-develop agents inside the organisation.
- Per-agent success heatmap and error timeline
- Platform health, model usage and cost
- Proxy to cloud models with your own keys, observed from one point
- Masking, anonymisation, full audit trail
Agentic AI executes real business processes in four steps.
- 01
Understand signals
Inputs from applications, APIs, documents, voice channels and events become execution-ready tasks.
ApplicationsAPIsDocumentsVoiceEvents - 02
Supervisor plans
Complex objectives are decomposed into structured steps; responsibilities are assigned to specialised agents.
SupervisorPlanningDecompositionDelegation - 03
Agents act
Knowledge is retrieved, documents processed, tools invoked, and real operations triggered in enterprise systems.
RetrievalTools / MCPActionsIntegrations - 04
Everything is observed
Agent decisions, latency, cost and tool usage are monitored and optimised with full visibility across environments.
TracesLatencyGuardrailsAudit
Eight integrated capability layers.
Built for enterprise scale, governance and resilience. The layers run as one platform.
Explore the platform →Orchestration & Execution Intelligence
Runs complex work end-to-end; routes requests intelligently and keeps multi-step processes stable even when dependencies fail.
BAI / Model Orchestration
Uses the right model for each task; reduces vendor lock-in and keeps operations running when a provider is unavailable.
CKnowledge & Retrieval
Turns fragmented enterprise information into governed, searchable, trustworthy knowledge; answers link back to sources.
DGovernance, Risk & Compliance
Makes AI auditable, safe and fit for regulated environments; every decision is traceable.
EObservability & Optimisation
Provides visibility into accuracy, speed, cost and health; produces the data for continuous improvement.
FIntegration & Connectivity
Connects to enterprise systems and customer channels without rebuilding the existing technology landscape.
GMultichannel Experience & Voice
Delivers AI across digital, messaging and voice channels on one consistent orchestration underneath.
HDeployment & Enterprise Architecture
Supports the on-premise, hybrid and sovereignty requirements of large enterprises.
Workloads running live on Aginies today.
E-commerce support & action
Where is my order, returns, delivery changes: the agent completes the transaction in the order system.
Read more → Enterprise FunctionsInvoice & document extraction
OCR → validation → ERP: clean records without manual entry.
Read more → InsuranceVoice AI for claims intake
A voice agent takes the loss notice and opens the claim in the system.
Read more → Software DeliverySDLC agent team
PM, analyst, architect, developer and tester agents working together in the CI/CD pipeline.
Read more → Enterprise FunctionsAgent outsourcing (agent workforce)
Outsourced agents instead of outsourced people: iOS developer, business analyst, accountant, tester; with a work log.
Read more → BankingConversational banking
Complete mobile banking operations by typing or talking.
Read more →77 live agents in the agent library →
Derived at build time from the agent library’s scenario definitions.
A disciplined transformation methodology.
We decide with numbers which AI ideas are worth building and when to start. Every use case passes a five-layer assessment; each stage ends at a clear decision point: proceed, fix or stop.
- 01 Value Why should this be done at all?
- 02 Readiness Do we have the inputs we need?
- 03 Feasibility Can we actually build this?
- 04 Operations How will it live after go-live?
- 05 Governance Is it secure, controlled and auditable?
Designed for regulated environments.
AI must meet the same standards as every other critical system. Built to the requirements of regulated sectors such as finance, insurance, healthcare, government and defence; closed-network and air-gapped deployment included.
Time to move from experimenting with AI to transforming with it.
In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.
- Your 2–3 priority problems
- Live demo of a similar scenario
- Roadmap starting with value analysis