Fly high with AI. Win in weeks.

Minimal lift for your teams—we handle 90% of implementation work using pre-built connectors, APIs, and low-code orchestration.

Your team's role (10-15 hours total):

• Provide secure access to systems and data (API keys, credentials)
• Review and approve data ownership decisions
• Approve change requests and deployment windows
• Participate in pilot testing and feedback

We handle: Integration, configuration, testing, production hardening, monitoring setup, and ongoing optimization. No need to hire AI engineers or data scientists.

We start from turnkey workflows for common use cases, then configure them to your policies, vocabularies, and KPIs—so you get both speed and fit-for-purpose behavior.

Pre-built workflows include:

• AI Service Desks: Automate tier-1 support, deflect 40-60% of tickets
• AI Copilots for Operations: Assist agents with real-time answers, reduce handle time by 30-50%
• AI Search: Find answers across documents, databases, and systems in seconds
• Churn Prediction: Flag at-risk customers 30 days early, cut churn by 28-31%
• Cart Recovery: Personalized recovery campaigns, recover 38-42% of abandoned carts
• Claims Processing: Automate document review, cut processing time by 47-73%

Configuration: We adapt workflows to your industry (Healthcare, Finance, Retail, SaaS), compliance requirements (HIPAA, SOX, GDPR), and business rules.

70% of AI projects fail to reach production—we reduce that risk to near-zero with proven reference architectures, domain-tuned prompts, and rigorous quality controls.

Our risk reduction approach:

• Pre-validated use cases: Start from proven workflows with 150+ successful deployments
• Evaluation pipelines: Test every model change against your data and quality benchmarks
• Human-in-the-loop controls: Review high-stakes decisions before automation
• Guardrails & safety checks: Prevent hallucinations, policy violations, and non-compliant outputs
• Continuous monitoring: Track performance, quality, and drift in real-time

Result: 80% fewer hallucinations, zero compliance violations in first 6 months, 4.8/5 customer rating across 150+ deployments.

Yes—we complement your existing teams and vendors instead of forcing a full rebuild. Our goal is to plug into your current stack and operating model.

Common integration scenarios:

• With internal teams: We handle AI implementation while your data, engineering, or analytics teams focus on core systems
• With cloud vendors: Deploy on AWS, Azure, or GCP—we work with your preferred cloud provider
• With CX platforms: Integrate with Salesforce, Zendesk, Intercom, or your existing service desk
• With data platforms: Connect to Snowflake, Databricks, or your data warehouse

Example: FinEdge (Finance) integrated our fraud detection AI with their existing Salesforce CRM and AWS infrastructure—deployed in 81 days, blocked $4.2M in fraud losses.

Yes—pricing scales with your size and complexity:

SMBs ($10M-$50M revenue): Start with Pilot ($4,900) or Growth ($9,900) for focused, turnkey packages with 90-day pilots. Includes 1-3 workflows, integration with 1-3 tools, and email/Slack support.

Mid-market ($50M-$500M revenue): Growth ($9,900) or Enterprise (custom, starting $49K) for broader solution bundles, higher usage limits, priority support (4-hour response), and compliance audit support (SOC 2, HIPAA, GDPR, SOX).

Both tiers: Same core platform, same value model, same outcome-based payment—adapted to your scale and complexity. No vendor lock-in, cancel anytime, export your data.

Zero vendor lock-in—contracts are structured to de-risk adoption. You can start with shorter terms or pilot programs and extend based on results.

Flexibility built-in:

• Start small: 90-day pilot ($4,900) with no long-term commitment
• Scale up: Move to Growth or Enterprise as you grow—no data migration
• Cancel anytime: No penalties, no hidden fees
• Export your data: You own your data, prompts, and configurations—take them with you
• Open standards: Platform runs on well-documented APIs, not proprietary black-box technology

Why? We keep customers because of value, not contracts. 150+ teams deployed, 4.8/5 rating, 62% average cost savings.

We optimize for cost by selecting the right mix of models, caching strategies, routing, and compression—so you maintain reliable outcomes while avoiding uncontrolled spend.

Cost optimization tactics:

• Model selection: Use smaller, faster models for simple tasks (e.g., GPT-3.5 for classification), larger models only when needed (e.g., GPT-4 for complex reasoning)
• Caching: Cache frequent queries to reduce LLM calls by 40-60%
• Routing: Route queries to the most cost-effective model based on complexity
• Compression: Compress prompts and context to reduce token usage by 30-50%
• Monitoring: Track usage and performance in real-time to detect cost spikes

Result: Predictable costs included in fixed pricing—no surprise $10K LLM bills at month-end.

Yes—we work with your stakeholders to define baselines, value drivers, and expected impact by use case. This includes cost savings, revenue lift, and risk reduction so you can align AI investments with P&L outcomes.

Business case components:

• Baseline metrics: Current costs, processing times, conversion rates, churn rates
• Value drivers: Cost reduction (30-50%), revenue lift (20-40%), efficiency gains (40-73%)
• ROI model: Expected savings vs. investment, payback period (typically 3-6 months)
• Risk assessment: Failure risk mitigation, compliance requirements, change management

Deliverable: Executive-ready business case with clear sponsorship from finance and leadership. Example: CloudSync's business case showed $2.4M ARR saved from 28% churn reduction—approved in 2 weeks.

Zero-downtime updates with rigorous testing—new models or prompts are validated against your data and quality thresholds before rollout.

Update process (4-step safety protocol):

1. Evaluation pipeline: Test new model/prompt against 500+ real queries from your data, measure accuracy, latency, and compliance
2. Shadow deployment: Run new version alongside current version for 7 days, compare outputs without affecting users
3. Controlled rollout: Deploy to 10% of traffic, monitor for regressions (hallucinations, policy violations, performance drops)
4. Full rollout: Expand to 100% only after passing all quality gates—with instant rollback capability if issues detected

Monitoring in place: Real-time alerts for quality drift, latency spikes, error rate increases, or compliance violations. You're notified before users are impacted.

Result: Zero production incidents from model updates across 150+ deployments—updates happen weekly without disrupting your operations.

Support tiers aligned to your criticality—from email support for pilots to 24/7 coverage for production-critical deployments.

Support by tier:

All tiers include: Proactive monitoring, automated alerts, incident reports, and post-incident reviews to prevent recurrence.

Enterprise-grade reliability with scalable infrastructure, health checks, observability, and fallbacks—so you can rely on AI for customer-facing or operational workflows without disruptions.

Reliability architecture:

• Multi-region deployment: AI services run across 3+ regions (US, EU, Asia) with automatic failover if one region goes down
• Model provider redundancy: Fallback across OpenAI, Anthropic, Google, and Azure if primary provider has outages
• Health checks: Continuous monitoring of latency (target: <2 seconds), error rates (target: <0.1%), and quality signals
• Auto-scaling: Infrastructure scales automatically to handle traffic spikes (e.g., Black Friday, tax season) without performance degradation
• Circuit breakers: Automatic isolation of failing components to prevent cascading failures

Observability: Real-time dashboards showing uptime, latency, error rates, and quality metrics—you see what we see.

Track record: 99.94% actual uptime across 150+ deployments in 2024, average incident resolution time: 47 minutes.

You retain 100% ownership of your data, business logic, prompts, and configurations. We provide the platform, orchestration, and operational tooling—you control the IP.

What you own:

• Your data: Customer data, documents, transaction history, analytics—never shared across customers or used to train public models
• Your prompts: Custom prompts, domain adaptations, and business rules you configure
• Your configurations: Workflow logic, integration settings, guardrails, and policies
• Your outputs: AI-generated responses, predictions, and insights belong to you

Data residency: Deployments comply with your data residency and retention policies (e.g., GDPR requires EU data stays in EU, HIPAA requires US data stays in US).

Export anytime: Download your data, prompts, and configurations in standard formats (JSON, CSV, SQL) with zero penalties or delays—no vendor lock-in.

Pre-built connectors for 50+ common systems used by mid-market and SMBs—embed AI into existing channels (web, chat, email, internal portals) without rebuilding your stack.

Integration categories:

Custom integrations: Need a system not listed? We build custom connectors via REST APIs, webhooks, or file transfers—typically added in 7-14 days.

Yes—vertically tuned solutions for regulated sectors (Healthcare, Finance) and customer-heavy industries (Retail, SaaS). Each deployment reflects your industry's terminology, compliance requirements, and KPIs.

Vertical-specific capabilities:

Domain expertise: Our team includes former operators from each vertical who understand your workflows, pain points, and success metrics—not just generic AI consultants.

Yes—white-label platform for agencies and service providers to deliver AI solutions under their own brand. Add AI capabilities to your portfolio, scale delivery, and serve more clients without building an AI stack from scratch.

White-label program includes:

• Your branding: Custom domain, logo, colors, and messaging—clients see your brand, not ours
• Partner pricing: 30-40% margin on all deployments, volume discounts for 5+ clients
• Sales enablement: Pitch decks, ROI calculators, case studies, demo environments
• Technical training: 2-day onboarding, certification program, ongoing support
• Co-delivery model: We handle AI implementation, you manage client relationship and change management
• Multi-tenant management: Single dashboard to manage all your clients, usage, and billing

Ideal for: Digital agencies, consulting firms, MSPs, system integrators serving mid-market and SMB clients.

Example: LiftWorks (Agency) scaled from 9 to 27 clients in 6 months using our white-label platform—without hiring AI engineers or data scientists.

Yes—startups can leverage our platform to launch AI products faster and with lower upfront cost. Avoid building foundation capabilities in-house and focus on differentiation, UX, and go-to-market.

Startup benefits:

• Fast launch: Deploy AI features in 30-60 days vs. 6-12 months building from scratch
• Low upfront cost: Start with Pilot ($4,900) or Growth ($9,900) vs. $200K+ to hire AI team
• Pre-built infrastructure: LLM orchestration, vector search, evaluation pipelines, monitoring—ready to use
• Compliance included: SOC 2, HIPAA, GDPR, SOX built-in—no need to build compliance from scratch
• Scale as you grow: Start small, upgrade to Enterprise as you add customers—no migration pain

Common startup use cases:

• AI-powered customer support for SaaS products
• Document processing for fintech or legal tech
• Personalization engines for ecommerce or content platforms
• Predictive analytics for B2B sales or marketing tools

Startup pricing: 20% discount on first year for pre-seed and seed-stage startups (under $2M raised)—apply with pitch deck and proof of funding.

Multi-layer data protection with encryption, access controls, PII detection, and zero data sharing—your data never trains public models or crosses customer boundaries.

Data protection measures:

• Encryption everywhere: Data encrypted in transit (TLS 1.3), at rest (AES-256), and in memory during processing
• Data isolation: Each customer's data stored in separate encrypted databases—no cross-customer access
• PII detection: Automatic detection and redaction of sensitive data (SSN, credit cards, health records) before processing
• Access controls: Role-based permissions, MFA required, audit logs for all data access
• Zero data sharing: Your data never used to train public models (OpenAI, Anthropic, Google) or shared with third parties
• Data residency: Choose where your data lives (US, EU, Asia) to meet regulatory requirements
• Retention policies: Automatic data deletion after retention period (configurable: 30, 60, 90 days, or custom)

Leak prevention: Content filtering blocks AI from exposing sensitive data in responses—tested against OWASP LLM Top 10 vulnerabilities.

Track record: Zero data breaches or leaks across 150+ deployments since 2022—independently verified in SOC 2 audits.

AI-specific security controls to prevent prompt injection, jailbreaks, data exfiltration, and other LLM vulnerabilities—tested against OWASP LLM Top 10 and real-world attack patterns.

AI security measures:

• Input validation: Filter malicious prompts, injection attempts, and adversarial inputs before reaching the model
• Output filtering: Block responses containing PII, credentials, internal system details, or policy violations
• Prompt isolation: System prompts separated from user inputs—prevents prompt injection and override attacks
• Rate limiting: Throttle suspicious activity (e.g., rapid-fire queries, enumeration attempts)
• Guardrails: Content policies enforced at runtime—block harmful, biased, or non-compliant outputs
• Model access controls: Only authorized services can call models—no direct user access to underlying APIs
• Adversarial testing: Quarterly red-team exercises simulating prompt injection, jailbreaks, and data exfiltration

Monitoring: Real-time detection of anomalous behavior (e.g., unusual query patterns, policy violations, data leakage attempts) with automated alerts and blocking.

Compliance: AI security controls documented and audited as part of SOC 2 Type II certification—ready for your security reviews.

Yes—flexible deployment models including your private cloud (AWS, Azure, GCP) or hybrid setups where sensitive data never leaves your environment.

Deployment options for sensitive data:

All deployment models include: Same security controls, compliance certifications, monitoring, and support—regardless of where the platform runs.

Setup time: VPC deployment: 14-21 days, Hybrid: 7-14 days, On-premise: 30-45 days (includes infrastructure setup and testing).

Bias detection, content policies, and compliance guardrails built into every deployment—so AI outputs meet your regulatory, ethical, and brand standards.

Bias and fairness controls:

• Bias testing: Evaluate models for demographic bias (race, gender, age) using standardized benchmarks before deployment
• Fairness monitoring: Track output distribution across user segments to detect disparate impact
• Human review: Sample outputs reviewed by domain experts to catch subtle bias or inappropriate content
• Feedback loops: Users can flag biased or problematic outputs—reviewed within 24 hours and used to refine guardrails

Compliance guardrails:

• Content policies: Block harmful, illegal, or non-compliant outputs (e.g., medical advice without disclaimers, financial advice without disclosures)
• Regulatory alignment: Guardrails tuned for your industry (e.g., HIPAA for healthcare, FINRA for finance, FTC for consumer-facing)
• Explainability: Audit logs show why AI made specific decisions—required for regulated industries
• Human-in-the-loop: Route high-risk decisions (e.g., loan approvals, medical triage) to human reviewers

Transparency: Disclose when users are interacting with AI (required by EU AI Act, California AB 2013, and other regulations).

Governance: Monthly bias and compliance reports with flagged outputs, policy violations, and remediation actions—ready for leadership and regulatory reviews.