“Building advanced AI is like launching a rocket. The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to focus on steering.”
Managed AI and Process Re-engineering for Enterprise Operations
Every AI system without continuous management loses accuracy, inflates costs, and accumulates risk. We don't just build AI, we operate, monitor, and optimize it in production so your ROI grows instead of erodes.
Book AI ROI Assessment30-60%
operational cost reduction
10x
faster process throughput
40-70%
automation of manual tasks
What Happens After Go-Live Without Managed AI
System is live. Metrics look great.
Accuracy is high, throughput is strong, the team celebrates the go‑live. But already, real‑world data begins diverging from training distributions.
Data drift begins. Accuracy quietly falls.
User behavior shifts. New document formats appear. Edge cases accumulate. Without monitoring, degradation is invisible until it’s expensive.
Costs spike. Errors surface. Confidence erodes.
Token budgets are blown. Hallucinations increase. Manual overrides multiply. The business questions whether AI was worth it. IT has no playbook.
ROI is negative. The project is quietly shelved.
A €200k implementation delivers no lasting value, not because AI failed, but because there was no operational discipline around it. This is the rule, not the exception.
Silent Risk
Data & Model Drift
Models trained on yesterday’s data make tomorrow’s mistakes. Without continuous drift detection and retraining, accuracy erodes silently across every prediction.
Financial Risk
Uncontrolled Compute Cost
Without token optimization, model selection tuning, and caching strategies, API costs multiply 2-3x within six months. Budgets disappear on inefficient calls.
Operational Risk
Hallucinations & Errors
Without prompt governance and knowledge base maintenance, LLM outputs degrade. In financial, legal, or support workflows, this means direct liability.
Regulatory Risk
Compliance Exposure
GDPR, ISO 27001, SOC 2 obligations evolve. AI systems without audit trails, access controls, and governance documentation create regulatory vulnerability.
The Difference Between
Implementing and Operating AI
Most companies treat AI like a software project. It isn’t. Managed AI is the operational discipline that turns a one‑time deployment into a growing business asset.
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One-time delivery
You get a system. Nobody operates it.
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No monitoring
Degradation is invisible until it’s a crisis.
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No retraining
Model accuracy falls as data shifts.
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Unpredictable costs
API spend grows uncontrolled.
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No SLA
Downtime and errors have no owner.
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No audit trail
Compliance exposure is unresolved.
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ROI erodes
Value peaks at go‑live, declines from there.
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Continuous operations
We run and evolve the system as a service.
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24/7 monitoring + alerts
Issues caught before they become incidents.
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Drift detection & retraining
Accuracy stays high as data evolves.
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Cost optimization built in
30-70% cost reduction through smart tuning.
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SLA & reliability guarantee
Uptime, response, and quality commitments.
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Full audit trail & governance
GDPR, ISO 27001, SOC 2 ready.
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ROI grows over time
Every month the system performs better.
The Right Fit
For Operations-Heavy Enterprise
We don’t build AI from scratch, we help you operate it right, re-engineer broken processes, identify weak spots, and implement or develop what’s actually needed.
Companies Already Using AI
Tools are deployed but ROI isn't materializing. You need an operational audit, process redesign, and a clear model for running AI as a business function.
- Low employee adoption across teams
- Manual work running parallel to AI
- No visibility into outcomes or metrics
Enterprises in Digital Transformation
Banks, insurers, retail, and manufacturing looking to rethink operations with AI, from process analysis and re-engineering to end-to-end automation.
- High cost of manual operations at scale
- Bottlenecks across cross-functional workflows
- No in-house expertise to evaluate solutions
Teams Without Internal AI Expertise
Startups, mid-market, and enterprise units that need a hands-on partner, from diagnosing gaps and selecting tools to building and running AI solutions end-to-end.
- Unclear where to start or what to prioritize
- Risk of picking the wrong tool or vendor
- Need both strategy and execution in one place
Designing & Operating Your AI-Driven Enterprise
We enter with a structured AI ROI assessment and operate the full lifecycle, not as an IT vendor, but as your AI operations partner. Every engagement ends with a managed service layer.
Schedule AI Transformation Call-
1
As-Is Assessment
1-2 WEEKS
We map your operations, quantify automation opportunity, and model ROI before a single line of code is written.
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2
To-Be Model
1-2 WEEKS
Based on the assessment, we design the target state: which processes to automate, how, and in what order. You get a clear picture of the future architecture and a prioritized roadmap with projected ROI.
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3
Proof of Concept
2-4 WEEKS
We implement AI on 2-3 high-priority processes to validate the model in real conditions. No heavy investment, no long commitments, just fast, measurable signals on what automation can deliver in your environment.
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4
Scale
SCALABLE
PoC validated. Economics confirmed. Now we roll the same approach across remaining processes, with production-grade deployment, governance, integrations, and observability built in from the start.
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5
Managed AI Operations & Optimization
ONGOING MRR
Continuous monitoring, retraining, cost tuning, and capability expansion. Your AI improves every month.
What's Included In Managed AI
Most companies treat AI like a software project. It isn’t. Managed AI is the operational discipline that turns a one-time deployment into a growing business asset.
Reliability & Monitoring
24/7 uptime monitoring, anomaly detection, alert response, and SLA enforcement. We own the system’s reliability so you don’t have to.
- 99.9% uptime SLA
- Incident response playbooks
Quality & Model Optimization
Continuous drift detection, model retraining, prompt engineering, and accuracy benchmarking. Your AI stays sharp as the world changes.
- Weekly accuracy reviews
- Retraining on new data
Cost Control & FinOps
Token optimization, intelligent model routing, caching layers, and latency tuning. Typically reduces AI compute costs 30–70%.
- 30-70% cost reduction
- Weekly cost reports
Governance & Compliance
Data access controls, full audit trails, explainability logging, and compliance documentation for GDPR, ISO 27001, and SOC 2.
- GDPR / ISO / SOC 2 ready
- Regulator-ready audit logs
Workflow & Agent Management
Updating automations, adding new process scenarios, expanding integrations, and managing human-in-loop decision flows.
- New scenarios, no re-scoping
- Multi-system integrations
Continuous Improvement
Monthly performance reviews, automation rate expansion, new use case identification, and ROI reporting to executive stakeholders.
- ROI grows every month
- Executive dashboards
Recent Managed AI Projects
We integrate AI into your existing systems and operate it continuously, taking ownership of accuracy, uptime, and optimization. No replacement. No internal ML team needed.
Finance
AI Process Orchestration for Claims Workflow
3х
more cases processed per officer
61%
reduction in average case resolution time
100%
of AI decisions logged with full audit trail
Challenge
A regional bank's loan amendment process required coordination across three legacy systems with heavy manual effort at every handoff. Case officers spent most of their time on data retrieval and cross-system validation rather than judgment-based review, with no path to automate without touching core platforms.
Our Approach
We built an AI orchestration middleware that coordinates data flow between all three existing platforms using APIs and RPA bridges. The AI prepares and packages every case fully, but human officers retain sign-off on all regulated decisions. Automation started with one standardized case type and expanded quarterly as compliance team confidence grew.
Applied Methods and Practices
- Multi-system orchestration via API and RPA bridges
- Rule-based and ML eligibility screening
- Human-in-the-loop at all regulated decision points
- Incremental automation expansion across case categories
Solution Features
- Cross-system data retrieval, validation, and case assembly
- AI-assisted eligibility and risk assessment with escalation triggers
- Automated approval routing and SLA tracking
- Immutable per-case audit log with regulator-ready export
Retail & B2B SaaS
Scaling Customer Support for a B2B SaaS Platform
62%
of L1 tickets resolved without agent involvement
44%
reduction in average handling time
+18 pts
CSAT improvement within 90 days
Challenge
With 8,000+ tickets per month growing 40% YoY, response times were slipping and CSAT had dropped to 61%. What appeared to be a staffing problem was actually process inefficiency, 60% of tickets were repetitive L1 queries answerable from existing but underutilized documentation.
Our Approach
Following a two-week As-Is assessment, we restructured the client's Confluence knowledge base and embedded it into a vector store for RAG. AI responses and routing decisions were surfaced natively inside Zendesk, zero workflow disruption, zero agent retraining. Managed operations were scoped from day one.
Applied Methods and Practices
- Intent classification across 40+ ticket categories
- Confidence-tiered routing (auto-resolve / assist / escalate)
- RAG over restructured knowledge base
- Weekly hallucination sampling and correction pipeline
Solution Features
- Full Confluence audit, restructuring, and vector embedding
- AI triage and response generation inside Zendesk
- Automated sync pipeline for ongoing knowledge updates
- Monthly CSAT, cost, and automation coverage reporting
Insurance
Managed AI for Invoice & Document Processing
85%
of documents processed without human
52%
reduction in cost per processed document
<90sec
average end-to-end processing time
Challenge
A shared service center processing 40,000+ documents monthly relied on manual review and legacy OCR with high error rates. The goal was to reduce manual effort and SLA delays without replacing or redeveloping the existing SAP and document management infrastructure.
Our Approach
We designed an AI middleware layer that integrates with the existing SAP and DMS environment via pre-built connectors. The engagement was scoped as Build + Operate from day one, Azati owns extraction accuracy, uptime SLA, and continuous improvement as the core delivery model, not optional support.
Applied Methods and Practices
- Multi-format document ingestion and classification
- AI-assisted field extraction with confidence scoring
- Human-in-the-loop routing for low-confidence cases
- Managed AI Ops with monthly cost and accuracy reporting
Solution Features
- Multi-channel ingestion (PDF, TIFF, DOCX, XML, EDI)
- SAP REST API integration with master data matching
- Immutable audit log per document with GDPR-compliant retention
- Live operations dashboard with cost-per-document tracking
Industries We Serve
Our Managed AI expertise spans high-value sectors where operational complexity creates the strongest ROI for AI-driven transformation.
Managed AI That Performs in Production
Move beyond one-off builds with AI systems we operate, monitor, and optimize for you.
Get a Free AssessmentThree outcomes that business leaders care about
ROI That Compounds Every Month
Unlike a one-time project that peaks at go-live, Managed AI compounds. Every month we optimize, expand, and improve, increasing value without proportional cost.
Scale Operations Without Growing Headcount
Process more cases with the same team. Reduce SLA times from days to minutes. Scale operations without proportional headcount growth.
MRR+
value grows every month
Reduce OPEX. Replace Manual Work with AI
Replace high-volume manual operations with AI automation. Reduce OPEX on document processing, support, and back-office workflows, with a clear before/after model.
5x
more throughput with the same headcount
Clients Feedback
What clients value most is continuity. Azati doesn't hand off and walk away, we integrate AI into existing workflows and operate it as a managed service, taking ownership of performance, compliance, and cost from day one through every stage of growth.
"We don't need anyone else; they are exactly who we need."
"I know that, if I have a question, their team will have an answer, or they will figure it out a lot faster than I could."
"Our users love the portal which is the reason we've been successful."
"They really know their craft."
"The app has never crashed, which is a testament to the quality of the code they put into this."
FAQ
An agency builds and leaves. We operate. After deployment, we own the system's performance, monitoring accuracy, optimizing costs, retraining models, and expanding capabilities. You get a long-term operations partner, not a one-time vendor.
Yes. We start with an audit, assess current accuracy, cost efficiency, and governance gaps. Then we build a managed operations layer on top of what exists. Most clients see measurable improvement within 60 days.
You get a live dashboard with accuracy metrics, cost tracking, uptime, and automation rate. Monthly reviews with your operations team. If something degrades, we catch it before you do.
Every decision is logged with a confidence score. Low-confidence cases route to human review automatically. Nothing critical runs without a fallback. Audit trails are available for every action.
Typically 6–10 weeks from pilot launch to first measurable savings. We model expected ROI before we start, so you know the number before committing to a full rollout.
Private deployment options, full data access controls, and audit trails are standard, not add-ons. We work with clients in banking, insurance, and healthcare under GDPR, ISO 27001, and SOC 2 requirements.
We structure engagements in fixed phases: Assessment, Pilot, then Managed Operations as a monthly service. No open-ended retainers. You approve scope before each phase. Managed AI is a predictable line item, not a variable project budget.