AI Automation Platform Evaluation & Selection Service

Platform choice cascades into three direct EBITDA levers: implementation speed, ongoing compute cost per automated transaction, and technical debt accumulation. PE-backed enterprises typically operate across 5-15 portfolio companies with heterogeneous tech stacks, legacy ERP systems, and varying data maturity. A platform that excels in standalone benchmark tests often fails when embedded in multi-entity workflows requiring audit trails, role-based access, and GDPR-compliant data residency.

Salyant's evaluation framework quantifies these hidden costs through workload simulation against your actual process maps — not vendor demos. We measure token consumption per business transaction, latency under concurrent load, and the engineering effort required to maintain prompt chains as models evolve. This data drives a total cost of ownership model that operating partners can present to investment committees with confidence.

• Workload simulation using actual portfolio process maps, not synthetic benchmarks
• Token economics modelling per automated transaction across OpenAI, Claude, Anthropic, and Make
• Technical debt scoring for prompt maintenance, version drift, and model deprecation risk
• Compliance mapping for UK GDPR, FCA regulatory reporting, and sector-specific data sovereignty

Each platform occupies a distinct position in the automation stack. OpenAI's API ecosystem offers the broadest model variety and function-calling maturity, but pricing volatility and context window limits affect high-volume document processing. Claude (Anthropic) excels in long-context reasoning and constitutional AI guardrails — critical for regulated workflows — yet lacks native workflow orchestration. Make (formerly Integromat) provides visual workflow automation with 1,000+ pre-built connectors but requires external LLM integration for cognitive tasks.

Salyant's comparison matrix evaluates 12 dimensions: model capability fit, integration surface area, data egress costs, SLA availability, UK data residency options, prompt engineering tooling, version control for prompts, testing frameworks, monitoring granularity, support tier responsiveness, contractual flexibility, and exit portability. We weight dimensions against your portfolio's specific use cases — invoice reconciliation, contract review, customer onboarding, regulatory reporting — rather than generic benchmarks.

• OpenAI: Highest model diversity, function calling maturity, variable latency, US-centric data residency
• Claude (Anthropic): Superior long-context reasoning, constitutional AI safety, limited native orchestration, emerging UK region
• Make: Visual workflow builder, extensive connector library, requires external LLM layer, EU hosting available
• Hybrid architectures: Salyant designs multi-platform deployments where each component serves its optimal function

Platform selection is only the first decision. The integration architecture determines whether automation scales across portfolio companies or stalls at pilot stage. Salyant delivers a phased roadmap: Phase 1 establishes a secure AI gateway layer with unified authentication, audit logging, and cost controls. Phase 2 deploys platform-specific adapters for each chosen provider, enabling hot-swapping as models improve or pricing shifts. Phase 3 implements portfolio-wide observability — tracking automation success rates, exception volumes, and cost per outcome across all entities.

Our architects design for the realities of PE portfolios: heterogeneous identity providers (Azure AD, Okta, legacy LDAP), mixed cloud/on-premise data sources, and the need for rapid carve-out readiness. Every integration includes infrastructure-as-code templates, automated regression testing for prompt changes, and documented runbooks for Level 1 support teams. This reduces dependence on scarce ML engineering talent and accelerates time-to-value for new platform acquisitions.

• AI gateway layer: unified auth, audit logging, cost controls, provider abstraction
• Platform adapters: hot-swappable interfaces for OpenAI, Claude, Anthropic, Make APIs
• Portfolio observability: cross-entity dashboards for success rates, exceptions, cost per outcome
• IaC templates & runbooks: Terraform modules, automated prompt regression tests, L1 support documentation
• Carve-out readiness: data isolation, contract portability, model export pathways

Vendor commercial terms vary significantly and directly impact portfolio P&L. OpenAI and Anthropic offer consumption-based pricing with volume discounts negotiated at enterprise scale — but require committed spend forecasts. Make operates on operation-based tiers with predictable monthly costs but limited SLA guarantees for mission-critical workflows. Salyant negotiates framework agreements on behalf of PE groups, leveraging aggregate portfolio volume to secure committed-use discounts, custom SLA tiers (99.9% uptime with <200ms p99 latency), and contractual off-ramps for model deprecation or regulatory change.

We structure SLAs around business outcomes, not infrastructure metrics: invoice processing accuracy >99.5%, contract review turnaround <4 hours, customer onboarding automation rate >80%. Penalties and credits tie to these KPIs. Our commercial models include shared-savings arrangements where Salyant's fees align with verified automation ROI — ensuring our incentives match your EBITDA targets.

• Framework agreements leveraging aggregate portfolio volume across PE group
• Outcome-based SLAs: accuracy, turnaround, automation rate — not just uptime
• Custom SLA tiers: 99.9% uptime, <200ms p99 latency, dedicated support channels
• Contractual off-ramps for model deprecation, regulatory change, vendor M&A
• Shared-savings fee models aligned with verified automation ROI

Our 6-week engagement delivers a board-ready platform selection report, integration architecture, commercial framework, and pilot deployment in one portfolio company. Week 1-2: process mining across 3-5 target workflows per entity, capturing volume, complexity, exception rates, and current unit economics. Week 3: workload simulation on all four platforms using production data samples, measuring token costs, latency, accuracy, and engineering effort. Week 4: integration architecture design with security review, data flow mapping, and compliance sign-off. Week 5: commercial negotiation with vendors, SLA definition, and framework agreement drafting. Week 6: pilot deployment in highest-impact portfolio company with automated monitoring and weekly business reviews.

Post-pilot, we provide a scale playbook for rolling across the portfolio: standardized adapter configurations, prompt libraries by use case, training programmes for internal automation champions, and quarterly platform health reviews. This methodology has been refined across UK mid-market manufacturers, business services roll-ups, and healthcare portfolios — delivering median 40% reduction in manual processing cost within 90 days of pilot go-live.

• 6-week fixed-scope engagement: selection report, architecture, commercials, pilot
• Process mining across 3-5 workflows per entity using production data
• Workload simulation on all platforms with token cost, latency, accuracy metrics
• Security review, data flow mapping, UK GDPR/FCA compliance sign-off
• Vendor negotiation, framework agreements, outcome-based SLAs
• Pilot in highest-impact entity with automated monitoring & weekly reviews
• Scale playbook: adapter configs, prompt libraries, champion training, quarterly reviews