Platform

One platform. Every layer of AI execution.

Build agents, manage knowledge, observe executions, connect systems. All from a single, production-grade control plane. Aginies is not a chatbot; it is the execution layer where all of the enterprise’s AI scenarios run.

Autonomous Agent

Build the agent by dialogue; test it, approve it, deploy it.

Describe what you need: the Autonomous Agent sets up the supervisor and specialist agents, connects tools and the knowledge base, picks the models, writes and runs the test corpus, and deploys with your approval. Everything in the agent library can be produced through this dialogue; runs are observed in logs and the dashboard.

Autonomous AgentAginies Workspace · aginies-large-c5

Message the Autonomous Agent…

AgentTestsLive

The agent will appear here

Test corpus: by suite, scenario and turn; golden path, branches, robustness injection
Check families: customer resolved, PII before verification, state machine, memory round-trip, judge dimensions
Coverage report: which checks score, which are blind
Changes are versioned; deployment always stops at a human approval
Logs, conversations and the dashboard are queried from the same dialogue

Product family

Five products, one platform.

Platform Aginies is the umbrella; Voice, Code, Hub and Dialog are its modules. Same governance, same logs, same licence.

Platform Aginies

Everything comes together here

Orchestration core, visual agent builder, Autonomous Agent, knowledge base, logs and dashboards. The single control plane where every agent is built, observed and governed.

Visual agent builder: agent, router, memory, guardrail and human-approval blocks
Autonomous Agent: build agents by dialogue, test corpora, deployment
Knowledge base: document pipeline, versioning, chunk editor
Logs and dashboards: live runs, conversations, per-agent success heatmap
Workspace and tenant separation: teams share agents inside the organisation and build together
Reporting: what happens per agent, platform health, model usage and cost
Templates, users, licences

Platform Aginies · Dashboard · last 7 daysworkspace: retail-ops · tenant: A

executions14.282

success%97,2

p50 latency1,4 s

platform healthhealthy

per agent · each cell ≈ 6 hours

kart_borc_odeme%99.1

Dialog_Banking%97.4

KVKK Personal Data Detection%96.2

KVKK Document Masking%98.8

insurance_bes_expert%100

satis_analiz_agent%100

model usage

aginies-large-c5$1.204

aginies-medium-oss-120b27%

aginies-small-oss-32b18%

aginies-voice-tr-v39%

aginies-large-g5$188

cost · by scenario

Credit assessment34%

Dialogue banking26%

KVKK masking18%

Claims intake14%

Other8%

PII masking: onanonymisation: onaudit trail: full3 workspaces · 2 tenants

Voice Aginies

STT · TTS · ASR

Speech models trained for Turkish: streaming speech-to-text, natural text-to-speech and real-time ASR. Voice agents through call-centre SIP integration.

aginies-voice-tr-v3 (STT), aginies-tts-tr-v2, aginies-asr-stream-v1
On-prem serving; audio never leaves the enterprise
Voice analysis: quality scoring, sentiment and intent

Voice Aginies · SIP · call 0212-…-4471aginies-voice-tr-v3 · on-prem

customerSomeone hit my car last night, plate 34 ABC 123, I want to report a claim.

agentSorry to hear that. I found your policy by plate; can I take the location and time?

STT180 ms

intentclaim intake · 0.97

sentimentcalm

TTS · aginies-tts-tr-v2ASR · stream-v1PII masking: on

Code Aginies

IDE Registry · IDE extensions · code completion

For engineering teams: IDE extensions and code completion, an enterprise IDE registry, and the SDLC agent team (PM, analyst, architect, developer, tester) in the CI/CD pipeline.

In-house code completion; code never leaves
IDE Registry: approved extension and model distribution
SDLC agents work with Jira, Git and CI

Code Aginies · billing-service/invoices.tscompletion · on-prem

14export async function exportInvoicePdf(id: string) {
15  const invoice = await repo.findById(id);
16  if (!invoice) throw new NotFound('invoice');
17  const pdf = await renderer.render('invoice', maskPii(invoice));
18  await audit.log('invoice.export', { id, user: ctx.user });
19  return storage.put(`invoices/${id}.pdf`, pdf);
20}

Tab: acceptPROJ-4821coverage 87%SAST ✓

IDE RegistryVS Code · JetBrains extensionDeveloper Agent · PR #2291

Hub Aginies

Model Hub · MCP Hub · Model proxy

Model catalogue: enterprise fine-tuned versions of proprietary models (c5, g5, g4), open-source-based on-prem models (oss), vision and speech models. Enter your own keys and let the platform reach cloud models as a proxy: single-point management, usage and cost tracking, masking. MCP Hub: approved tool servers.

aginies-large-c5 · medium-c5 · large-g5 · large-g4 · medium-g4
aginies-medium-oss-120b · small-oss-32b · vision-oss-72b (on-prem)
Model proxy: cloud models with your own keys, observed and masked from one point
Switch models with one selection; fallback strategy

Hub Aginies · Model Hubworkspace: finance-ops

model	access	usage · 7 days	cost
aginies-large-c5	proxy · your key	62%	$412
aginies-large-g5	proxy · your key	38%	$288
aginies-medium-oss-120b	on-prem · GPU 2×	84%	—
aginies-small-oss-32b	on-prem · GPU 1×	91%	—
aginies-voice-tr-v3	on-prem · STT	47%	—
aginies-vision-oss-72b	on-prem · vision	22%	—

keys encrypted · never shown to usersall requests maskedMCP Hub · 14 servers

Vision Aginies

Documents · images · masking

Image and document understanding: invoice, contract and form extraction, damage-photo assessment, visual quality control. Personal-data detection and document masking run on the same models.

aginies-vision-oss-72b · on-prem
Field extraction with source document and confidence score
Personal-data detection and document masking

Vision Aginies · invoice_2026_0912.pdfaginies-vision-oss-72b · on-prem

extracted fields

Invoice noFTR-2026-0912

Date12.09.2026

KDV%20 · 3.125,00

Total18.750,00 TL

TCKNmasked (KVKK)

source: page 1confidence ≥ 0.95

Dialog Aginies

Dialogue UI

The dialogue surface for customers and employees: web widget, mobile, WhatsApp and messaging. Session memory, human takeover and an audit trail for every message.

The same agent in text and voice channels
Human takeover and hand-back
Conversation dashboard and quality scoring

Dialog Aginies · web widgetkart_borc_odeme v2

I want to pay my full card balance from my current account.

Your statement balance is 24,860, due 14 September. Current account ****7741 has enough balance. If you confirm I will send a one-time code.

✓ get_card_debt · get_accounts · policy: no approval needed

Confirm.

Code sent. Once entered, the payment executes instantly and you get a confirmation.

session memoryhuman takeover: readyaudit trail

Type a message…↑

Architecture

Wherever it needs to run.

Same platform, same governance; different perimeter. Switch the deployment mode to see where the sovereign boundary falls.

Platform, models and data inside the enterprise data centre. No external dependency; open-source models served in-house.

Conceptual view. Component names and responsibilities are shown; implementation details are shared in a discovery session.

Capability layers

Eight layers, one execution model.

Orchestration & Execution Intelligence

the engine layer

Runs complex work end-to-end; routes requests intelligently and keeps multi-step processes stable even when dependencies fail.

Supervisor → sub-agent → supervisor architecture
Dynamic agent routing
Stateful multi-step execution
Agent-to-agent communication (A2A)
Scheduled and webhook triggers
Agent templates
Autonomous Agent: build agents by dialogue with test corpora

AI / Model Orchestration

brain selection and resilience

Uses the right model for each task; reduces vendor lock-in and keeps operations running when a provider is unavailable.

Multi-model gateway
Model proxy: single-point, observable access to cloud models with your own keys
Model switching without redesign
Fallback model strategy
On-prem serving for open-source models
Prompt testing environment and versioning
Hub Aginies: enterprise fine-tuned models (c5, g5, g4, oss) and speech models

Knowledge & Retrieval

enterprise memory

Turns fragmented enterprise information into governed, searchable, trustworthy knowledge; answers link back to sources.

Document embedding pipeline
Versioned knowledge base management
Tag-based document segmentation
Hybrid search and multi-source RAG
Chunk-level inspection and editing
Hallucination-reduction architecture
Knowledge base management: sources, tags and access policy

Governance, Risk & Compliance

the control layer

Makes AI auditable, safe and fit for regulated environments; every decision is traceable.

Guardrails and policy engine
Human-in-the-loop checkpoints
Agent decision traceability
Sensitive data masking and PII redaction
Masking and anonymisation of all requests
Full input/output logging and audit trail
Kill-switch and automation limits

Observability & Optimisation

performance management

Provides visibility into accuracy, speed, cost and health; produces the data for continuous improvement.

Per-run traces and timelines
Latency and token tracking
Cost attribution by scenario, unit and channel
Usage and cost per model; platform health
Routing accuracy measurement
Dashboard and analytics layer
Live logs, conversation records, per-agent success heatmap

Integration & Connectivity

enterprise reach

Connects to enterprise systems and customer channels without rebuilding the existing technology landscape.

REST API and database integrations
Model Context Protocol (MCP) servers
Custom tool definitions and invocation policies
Native connectors: email, calendar, files, collaboration tools
Web widget and messaging integrations
MCP Hub: catalogue of approved tool servers

Multichannel Experience & Voice

customer interaction layer

Delivers AI across digital, messaging and voice channels on one consistent orchestration underneath.

Real-time speech-to-text and streaming voice processing
Call-centre SIP integration
WhatsApp, SMS and email services
Intent detection and entity extraction blocks
Session memory
Dialog Aginies: dialogue UI, human takeover
Voice Aginies: Turkish STT, TTS and ASR models

Deployment & Enterprise Architecture

fit to enterprise constraints

Supports the on-premise, hybrid and sovereignty requirements of large enterprises.

On-premise and air-gapped deployment
Hybrid architecture: sensitive workloads inside, elastic capacity outside
Private cloud / dedicated region
SSO / SAML, fine-grained RBAC, workspace isolation
Per-tenant agent sharing and collaborative development
GPU-as-a-Service for in-country elastic capacity

Visual agent builder

Design agent pipelines without writing glue code.

Drag, connect, configure. Every block, from agents and routers to memory, guardrails and human approval, is a first-class primitive you compose into any workflow. Business and IT look at the same screen.

Agent

A specialist with its own model, tools and memory.

Supervisor

Breaks the objective into steps, delegates to agents, merges the result.

Router

Branches the flow by intent, rule or score.

Memory

Shares session and long-term context across agents.

Guardrail

Checks input and output against policy; blocks or masks.

Human approval

Pauses on critical decisions, presents to the authorised person, records the decision.

Tool / MCP

Read or write to an enterprise system; invocation policy per tool.

Parallel & loop

Runs independent steps simultaneously and repeated work until a condition is met.

Observability and reporting

AI is no longer a black box.

What happens per agent, the health of the platform, which model is used how much and what it costs, on one dashboard. A step-by-step record of every run: input, output, model, duration, tokens, approver. Every request can be masked and anonymised; everything is separated by workspace and tenant.

Per-agent executions, success and error heatmap
Platform health: latency, queues, GPU and model availability
Usage and cost per model, including cloud models through the proxy
Cost attribution by scenario, unit and channel
Masking and anonymisation on every request
Workspace / tenant separation; teams share and co-develop agents
One report for internal audit: model version, rule set, user

Platform Aginies · Dashboard · last 7 daysworkspace: retail-ops · tenant: A

executions14.282

success%97,2

p50 latency1,4 s

platform healthhealthy

per agent · each cell ≈ 6 hours

kart_borc_odeme%99.1

Dialog_Banking%97.4

KVKK Personal Data Detection%96.2

KVKK Document Masking%98.8

insurance_bes_expert%100

satis_analiz_agent%100

model usage

aginies-large-c5$1.204

aginies-medium-oss-120b27%

aginies-small-oss-32b18%

aginies-voice-tr-v39%

aginies-large-g5$188

cost · by scenario

Credit assessment34%

Dialogue banking26%

KVKK masking18%

Claims intake14%

Other8%

PII masking: onanonymisation: onaudit trail: full3 workspaces · 2 tenants

Integration

Plug in any tool with MCP.

Add a server URL, configure headers, test the connection: agents can invoke its tools securely. With chunk-level control, every piece of knowledge is inspectable, editable and tagged.

serverhttps://mcp.erp.internal/v1connected

toolsget_purchase_order · post_invoice · get_vendor

policypost_invoice → human approval > 50,000

authSSO · workspace: finance-ops

chunks4.212 · tag: policy/v3

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.

Book a 30-minute discovery session → dahi@aginies.com

Your 2–3 priority problems
Live demo of a similar scenario
Roadmap starting with value analysis