Enterprise Partnerships
Web3 Field
Data Platform — Building User-Co-Created Models with Vana + SightAI
Client Context: A health app wants personalized diet and fitness advice, but user data sits across Reddit, Oura, etc., controlled by large platforms.
Pain Points: Fragmented data limits model quality; privacy concerns block sharing.
Solution: Partner with Vana (MIT-incubated), which runs a user-owned data network where individuals upload data and control usage; AI developers propose model ideas and, upon consent, share in model ownership. The platform has surpassed 1M+ users and 20+ data DAOs. The enterprise then trains health models using those DAOs via SightAI, and benchmarks multiple LLMs through OpenRouter to choose the best for Q&A/explanations/generation.
Outcomes: Personalization accuracy ↑ ~25%; higher user engagement; users earn dividends from model ownership, increasing willingness to contribute data and creating a positive flywheel.
Digital Asset Manager — Enhancing BTC Yield with Babylon + SightAI
Client Context: An institution holds idle BTC and seeks stable yield while retaining self-custody.
Pain Points: Many BTC yield products use cross-chain or wrapped assets with bridge risk; limited in-house expertise on yield options.
Solution: Adopt Babylon’s native Bitcoin staking—stake BTC directly to secure other chains and earn rewards, with no wrapping or bridging, fully self-custodied and unbondable at will. (As of Aug 2025, Babylon reports 56k+ BTC staked, ≈$5.6B.) On top, use SightAI (via OpenRouter) to test models for on-chain yield analysis, risk assessment, and trend forecasting, and to optimize staking/un-staking.
Outcomes: Double-digit annualized returns while preserving ownership and security; better timing on stake/unstake; higher client trust and retention.
Logistics — Real-Time IoT Intelligence with IoTeX + SightAI
Client Context: A global logistics firm monitors cold-chain temperature, humidity, and location, and wants AI to optimize routing/scheduling in real time.
Pain Points: Conventional AI struggles with physical-world signals; IoT data is hard to make verifiable on-chain.
Solution: Integrate IoTeX’s DePIN network. IoTeX (a DePIN pioneer) expanded with IoTeX 2.0 (2024) and in 2025 bridges DePIN with AI. With QuickSilver, real-world device data connects to LLMs and can be accessed across chains at low latency. The logistics firm uses SightAI to orchestrate best-fit models (via OpenRouter) for real-time analytics/predictions and feeds instructions back to IoTeX devices.
Outcomes: Delivery delays ↓ ~50%; spoilage materially reduced; faster incident response; higher customer satisfaction; DePIN ecosystem incentives captured.
Cross-Border Marketplace — Instant Settlement with TradeOS + SightAI
Client Context: A C2C/B2B marketplace wants faster settlement and AI agents for negotiation and risk control.
Pain Points: Incumbents (eBay/PayPal) charge 10–20% fees and settle in 10–30 days; multilateral trade relies on central platforms with low trust.
Solution: Integrate TradeOS for decentralized escrow & payments. Built on zk-TLS, TradeOS enables the first permissionless escrow protocol for on-chain “Alipay”-like flows: no central operator, C2C/B2B across any TradeOS market; Proof-of-Delivery releases funds upon receipt; execution via smart contracts. In parallel, SightAI (via OpenRouter) powers multilingual agents for translation, fraud detection, and personalized bargaining.
Outcomes: Settlement time drops from days to real-time; fees ↓ >10%; AI agents boost close rate and reduce fraud; GMV grows.
Internet & E-Commerce
Large E-Commerce Platform — Support & Ticket Automation
Scenario: Peak-season multilingual inquiries (ZH/EN/JA), high FAQ repetition; few complex disputes with outsized impact.
Pain Points: Always using a strong model is costly and jittery; low FAQ hit rate wastes tokens; satisfaction varies across languages.
Solution with SightAI:
Multi-model routing: Intent → three lanes (FAQ → light model; product Q&A with RAG → mid model; refunds/disputes → strong model).
Token control: Vector-retrieve only “answer evidence fragments”; summarize history instead of replaying full threads.
High stability & scale: Geo-nearby entry; fast timeout fallback/degeneration; auto horizontal scale at peaks.
Caching & reuse: Cache hot questions/SKU answers → zero inference cost on hit.
Security & compliance: PII redaction; calls/prompts logged to a verifiable, decentralized audit trail.
Results (typical ranges):
Model spend −50% to −70% (≥60% sessions served by light/mid models; tokens −20% to −35%).
P95 latency −25% to −40%; agent throughput +30% to +60%; multilingual CSAT up slightly.
Why SightAI: Price-performance routing + caching + failover in one API—cheaper and more stable than direct vendor calls.
DTC Brand — Ads & Creative at Scale
Scenario: Weekly product drops; multilingual titles, bullets, long descriptions, ad copy, and A/B variants.
Pain Points: Manual/strong-model-only workflows are expensive; hard to keep tone consistent; limited A/B capacity slows launch.
Solution with SightAI:
Layered generation: Short titles/bullets → small model; long descriptions → mid model; brand-tone QA (10% sampling) → strong model.
Snippet reuse: Retrieve best-performing historical copy; templatize prompts; reuse “style fragment IDs.”
Batching & cache: Holiday/scene templates cached; 1→N variants by SKU class.
Scale: Parallel jobs with streaming; autoscale backends as needed.
Safety: Redact assets/prompts; auditable outputs.
Results (typical ranges):
Content throughput +3× to +5×; cost per 1,000 items −60% to −70%; CTR +5% to +12%.
Why SightAI: Aggregate many models—use the cheapest that’s “good enough,” then sample-and-guardrail with a stronger model for best ROI.
Cross-Border E-Commerce — Ops Analytics Assistant
Scenario: Massive logs, tickets, reviews, and ad metrics; ops teams drown in weekly summaries and reporting.
Pain Points: Fragmented data; manual prep is slow; feeding full context is expensive; delayed insights hurt decisions.
Solution with SightAI:
Complexity-aware routing: Routine weekly reports → small/mid model; escalate only “anomalies/critical hypotheses” to strong model for explanations.
Token control: Compute top KPIs/anomalies first; show the model “fragments + metric snapshots” only.
Dashboards: Auto-generate highlights/risks/actions; write references & calculations to the audit log.
Scale: Batch/parallel with retry/fallback to guarantee Monday delivery.
Results (typical ranges):
Report time −50% to −70%; ops productivity +2×; per-report model spend −40% to −60%.
Why SightAI: Routing + fragmented input achieves “same quality at lower cost” or “more output under the same budget.”
Financial Services
Group Shared Service Center — 3-Way Match & Expense Audit
Scenario: At scale—invoice/PO/receipt matching; high-volume reimbursements with audit pressure.
Pain Points: Manual checks are slow and error-prone; sending all docs to a strong model is costly and unstable; weak audit trails.
Solution with SightAI:
Tiered pipeline: OCR/field extraction → small model; rules matching → small model + rules; escalate only anomalies to a strong model for explanations & evidence chains.
Token control: Feed structured tables + delta fields; summarize long emails/contracts before compare.
Scale: Fast fallback; parallel batches; month-end autoscale.
Security & audit: Sensitive fields redacted; full process (instructions, citations) to verifiable audit logs.
Results (typical ranges):
Reconciliation throughput +3× to +5×; error rate −70% to −85%; audit prep time −50% to −65%; model spend −50% to −65%.
Why SightAI: Anomaly-first, split-weight processing spends money where it matters—end-to-end traceable.
Public Company Finance — Cash-Flow & AR Forecasting
Scenario: Multi-line, multi-currency; aging AR; needs rolling forecasts with explanations.
Pain Points: Legacy models update slowly; analysts spend time gathering/writing; feeding full tables is expensive.
Solution with SightAI:
Dual-model approach: Value model for coarse forecasts; escalate uncertain segments to a strong model for narratives & scenarios.
Token control: Provide “metric snapshots + anomaly fragments” only; incremental learning from forecast vs. actuals.
Scale: Scheduled batch runs with streaming charts; auto-fallback to last-known-good model on failure.
Audit: Parameters, versions, prompts, and outputs fully logged for reproducibility.
Results (typical ranges):
MAPE −20% to −35%; analyst time −40% to −60%; per-report model spend −40% to −55%.
Why SightAI: Spend on explanations only where hard—run everything else on cheaper paths.
AI Compute & “Intelligent” Data Centers
Unified Inference Gateway, Productized
Scenario: An AI center operates owned/hosted GPU clusters for external inference; global customers with diverse model preferences; needs cost-down + retention-up.
Pain Points: Single-model/single-entry leads to uneven utilization; cross-region latency swings; complex billing/rate-limit/isolation; heavy ops and audit burden.
Solution with SightAI:
Multi-model aggregation & policy routing: One API aggregates many providers; route by price/latency/quality thresholds with auto best-effort selection.
Geo-near & elastic scale: Multi-PoP routing; scale up at peaks, down at troughs; fast timeout fallback.
Billing & isolation: Visual billing; quotas & rate limits per tenant/BYO-Key/project.
Cache & batching: Cache hot requests; batch/merge calls to cut egress and inference costs.
Security & audit: Calls, prompts, citations, versions recorded to decentralized audit logs; sensitive fields redacted.
Results (typical ranges):
GPU utilization +15% to +35%; cost per request −30% to −55%; cross-region P95 latency −20% to −40%; higher renewal rates.
Why SightAI: One entry point delivers “multi-model choice + global traffic engineering + audit & billing”—faster, cheaper, and more stable than bespoke builds.
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