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AI‑Based Automation Implementation Services
Salyant partners with forward‑looking enterprises to design, build and scale AI‑driven automation that reshapes core processes, reduces manual effort and unlocks new value streams. Our end‑to‑end service combines rigorous business analysis, secure data engineering, production‑grade model deployment and continuous governance.
INSIDE THIS PAGE
What the existing page covers
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Our Service Framework
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Phase 1 – Discovery & Business Case
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Phase 2 – Data & Model Engineering
CONTINUING DOWN THE PAGE
More of the existing sections
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Phase 3 – Integration & Orchestration
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Phase 4 – Governance, Monitoring & Optimisation
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Service Level Commitments
Key Takeaway for Decision Makers
How long does a full‑scale AI‑based automation implementation take for a large enterprise?
A typical enterprise‑wide AI automation programme follows a phased roadmap of 12‑18 months from initial discovery to production‑grade operation, with each phase averaging 3‑4 months depending on data readiness and integration complexity.
Grounding Evidence: Industry benchmarks from Gartner (2023) and McKinsey (2022) show that the median time to value for AI‑enabled process automation across Fortune‑500 firms is 14 months, with a variance of ±2 months driven by data maturity and legacy system integration depth.
Our Service Framework
We deliver AI automation through a repeatable, four‑phase framework that aligns technology with business outcomes and provides transparent governance at every step.
- Phase‑gated milestones and gate reviews
- Cross‑functional delivery teams (strategy, data, engineering, change)
- Risk‑based security and compliance controls
- Outcome‑focused KPIs embedded in SLAs
Phase 1 – Discovery & Business Case
We work with senior stakeholders to map high‑impact processes, quantify automation potential and build a robust business case that justifies investment and defines success metrics.
- Process mapping workshops with COOs, line managers and IT leads
- Quantitative ROI modelling (cost‑to‑serve, cycle‑time reduction, error rate)
- Data audit and feasibility assessment
- Prioritisation matrix aligned to strategic objectives
Phase 2 – Data & Model Engineering
Our data engineers create a secure, governed data pipeline while our AI scientists develop, validate and benchmark models that meet enterprise‑grade performance standards.
- Data ingestion from ERP, CRM, IoT and document sources
- Automated data quality profiling and remediation
- Model selection using proven frameworks (e.g., TensorFlow, PyTorch, Spark ML)
- Rigorous validation against industry‑standard metrics (F1 ≥ 0.85, latency ≤ 200 ms)
Phase 3 – Integration & Orchestration
We embed AI models into existing workflows using API‑first design, RPA bots and orchestration platforms, ensuring seamless hand‑off between human and machine.
- REST/GraphQL API layer with OAuth2 security
- Robotic Process Automation (UiPath, Automation Anywhere) for UI‑level actions
- Event‑driven orchestration via Apache Kafka or Azure Service Bus
- User‑interface enhancements and change‑management training
Phase 4 – Governance, Monitoring & Optimisation
Post‑deployment, we establish continuous monitoring, drift detection and model retraining pipelines, backed by formal governance processes.
- SLA‑driven uptime ≥ 99.5 % and latency guarantees
- Automated performance dashboards (KPIs, cost‑per‑transaction)
- Model drift alerts and scheduled retraining (quarterly or as‑needed)
- Compliance audit trails for GDPR, ISO 27001 and industry‑specific regulations
Service Level Commitments
Our contracts include clear, measurable service levels that protect your investment and ensure predictable outcomes.
- 99.5 % service availability for production APIs
- Maximum mean‑time‑to‑recover (MTTR) of 4 hours for critical incidents
- Quarterly performance review meetings with executive sponsors
- Dedicated technical account manager (TAM) for escalation handling
Value Delivered
Enterprise clients typically realise a combination of cost savings, speed gains and quality improvements that translate into tangible ROI within the first 12 months of production.
- 20‑30 % reduction in manual processing time
- 10‑15 % decrease in error‑related rework costs
- Accelerated decision cycles (up to 3× faster)
- Scalable model architecture that supports future use‑case expansion
Frequently Asked Questions
What types of processes are best suited for AI automation?
High‑volume, rule‑based or data‑intensive processes with clear decision criteria—such as invoice processing, customer onboarding, claims adjudication and supply‑chain demand forecasting—deliver the quickest ROI. We assess each candidate during the Discovery phase.
How does Salyant ensure data security and regulatory compliance?
All data pipelines are built on encrypted, role‑based access controls and comply with GDPR, ISO 27001 and sector‑specific standards (e.g., HIPAA for health). We conduct a compliance audit at each phase gate and provide full audit trails.
Can existing legacy systems be integrated without a full replacement?
Yes. Our API‑first approach and RPA layer enable non‑intrusive integration with legacy ERP, mainframe or bespoke applications, preserving existing investments while adding AI capabilities.
What ongoing support is included after go‑live?
Our Service Level Commitments cover 24/7 monitoring, incident response, quarterly performance reviews and a scheduled model‑retraining cadence. Optional managed‑service extensions provide full‑stack support beyond the initial warranty period.
How is project pricing structured?
We use a transparent, milestone‑based pricing model: a discovery fee, fixed‑price engineering phases, and a usage‑based component for cloud compute and model inference. This aligns cost with delivered value and reduces upfront risk.
Accelerate Your Enterprise with AI‑Driven Automation
Contact Salyant today to design, deploy, and scale intelligent automation that delivers measurable ROI.