Here’s a crisp menu you can take to market for SoftQuantus + your SynapseX Hybrid LLM stack.

1) Core service lines (what we sell)

• AI Strategy & Discovery

  • Use-case portfolio, data/readiness audit, ROI modeling, roadmap.

• Custom LLMs (SFT/QLoRA/RLAIF/DPO)

  • Domain-tuned models, safety layers, evaluation harness (MT-Bench/MMLU + business KPIs).

• RAG & Knowledge Systems

  • Document ingestion, vector DBs (pgvector/Qdrant), hybrid search + quantum reranker, governance.

• Agentic Automation & Copilots

  • Process agents (tickets, finance ops, HR), tool-use, workflow orchestration, human-in-the-loop.

• Multimodal AI

  • OCR/doc intelligence, image/audio pipelines (Whisper/CLIP), call summarization, content QA.

• MLOps & Platform Engineering

  • CI/CD, monitoring, A/B eval, cost/tokens observability, vLLM/TGI serving, autoscaling.

• Secure Deployment

  • On-prem/air-gapped, Azure/AWS/GCP, BYOK, data retention, audit logs, policy filters.

• Training & Enablement

  • Prompt engineering, RAG best practices, platform admin, change management.

• Advisory & Compliance

  • Model risk mgmt, bias & safety tests, regulatory mapping (e.g., banking/health).

2) Industries → sample use-cases

3) Service delivery models

• Project-based (fixed scope): discovery → pilot → production.
• Managed service (MSP): we operate your SynapseX (SLA, scaling, updates).
• SaaS / API: OpenAI-compatible endpoint (usage-based pricing).
• Platform licensing: deploy in your VPC/on-prem with support tiers.
• Outcome-based / Rev-share: tied to savings or revenue lift (select cases).
• Training & Support: workshops, enablement, L3 support, success packages.

4) ROI levers & how to measure

Direct savings

• Ticket deflection / automation rate (%), AHT reduction (min), FTE hours saved.
• Infra optimization (tok/s ↑, latency ↓, GPUs ↓ via quantization/serving).
• Rework/error reduction (QA findings, refunds, compliance exceptions).

Revenue uplift

• Conversion/attach-rate ↑ with guided sales/copilots.
• Retention/CSAT/NPS ↑ with better self-service & faster resolution.
• Content velocity (docs/proposals/features shipped).

Risk reduction

• Compliance adherence (policy hits, audit success), PII leakage ↓, SLA breaches ↓.

Template formulas

• Cost savings = (Tickets automated × cost/ticket) + (Hours saved × loaded hourly rate) + (Infra cost reduction).
• Revenue lift = (Δ conversion × traffic × avg order value) + (Upsell rate × base sales).
• Payback = Project cost / Monthly net benefit; ROI = (Annual benefit – cost) / cost.

5) Starter packages (fast path)

• Discovery Sprint (1–2 weeks): 3–5 use-cases, data audit, ROI model, target architecture.
• Pilot (4–6 weeks): Small dataset SFT/QLoRA, RAG prototype, metrics dashboard, business A/B.
• Production Rollout (6–10 weeks): Hardening, safety, CI/CD, monitoring, SLOs, runbook.
• Continuous Optimization: Monthly evals, dataset refresh, prompt/agent tuning, cost optimization.

6) Why SynapseX Hybrid

• OpenAI-compatible API: drop-in for existing apps.
• Hybrid reranking (incl. quantum): better answer selection under noisy contexts.
• Trainability: SFT/QLoRA/DPO pipelines; merge & quantize for cost-efficient serving.
• Flexible deploy: on-prem/VPC/Azure Container Apps; GPU/CPU; vLLM/TGI.
• Governance-ready: moderation hooks, audit logs, PII redaction, feature flags.

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Example mapping: Service → Industry → ROI

• Claims triage copilot (Insurance, Managed Service) → 30–50% faster triage, −15% leakage; payback < 3 months.
• Contract review assistant (Legal, Project→Platform) → −60% review time, fewer missed clauses; auditors’ acceptance ↑.
• Tech-support deflection (Telco, SaaS/API) → 25–40% deflection, −AHT; CSAT +8–12 pts.
• Field maintenance copilot (Manufacturing, Platform) → −unplanned downtime, MTTR ↓; parts/inventory optimized.

If you’d like, I can tailor this into a one-page PDF offer sheet per industry (with KPIs and sample pricing tiers).