We build marketing AI that performs beyond the demo dataset
Marketing AI demos are convincing until real customer data arrives. Incomplete CRM records with duplicate contacts. Campaign histories spanning three platforms. Attribution gaps nobody can reconcile. We build marketing AI that handles this complexity.
You're here because your marketing AI isn't production-ready.
Demo-to-production gap?
Clean sample data works. Real CRM exports break everything – duplicate contacts, missing fields, inconsistent tags, and data nobody verified.
Personalization failures?
Your AI sends the wrong message to the wrong segment. One bad email to enterprise prospects and sales loses months of relationship building.
Lead scoring gaps?
Scores look good in reports but sales rejects the leads. The model learned from historical data that no longer matches actual buyer behavior.
Attribution black holes?
First-touch, last-touch, multi-touch – none of your models capture the actual customer journey. The data exists but nothing connects it.
Integration fragility?
HubSpot, Salesforce, ad platforms, CDP – each integration adds failure points. It worked in staging. Production data volumes exposed the gaps.
Scaling breaks everything?
A thousand contacts worked fine. A hundred thousand revealed latency issues, rate limits, and costs you didn't anticipate. The architecture can't keep up.
We've solved each of these problems in production – for lead scoring systems, campaign personalization engines, and marketing analytics platforms.
Here's how we solve these problems:
Test datasets built from real marketing data
Duplicate contacts, incomplete fields, inconsistent tagging, attribution gaps. The AI gets tested against what it will actually encounter.
Personalization guardrails before messages ship
Segment validation, audience matching, and confidence thresholds catch mis-personalization before it damages customer relationships.
Lead scoring calibrated against conversions
Continuous feedback loops from sales outcomes. Models retrain on what actually converts – not what historically looked promising.
Attribution that acknowledges uncertainty
Probabilistic models with confidence scores. When data can't definitively attribute, the system says so instead of fabricating certainty.
Integration patterns proven across platforms
HubSpot, Salesforce, Marketo, ad platforms, CDPs – we know which integrations break and why. Battle-tested patterns only.
Observability that surfaces problems first
Processing metrics, data quality monitoring, drift detection. Failures get flagged before campaigns go out. Debug any issue from logs alone.
Most teams ship marketing AI that works in demos. We build systems that work when real customer data arrives. Get the AI Launch Plan or schedule a consultation to learn more.
Complete marketing AI engineering capabilities.
Marketing Conversational AI
Lead Qualification Agents
Chat and voice agents that qualify inbound leads 24/7. Dynamic questioning, CRM integration, and handoff to sales.
Customer Support Automation
AI that handles marketing platform support queries. Escalation paths for complex issues. Full conversation history.
Campaign Assistant Agents
Conversational interfaces for campaign creation, optimization suggestions, and performance analysis.
Marketing Data & Analysis AI
Lead Scoring Systems
Predictive models that learn from actual conversion data. Continuous calibration against sales outcomes.
Campaign Performance Analysis
AI that identifies what's working across channels. Pattern detection beyond standard analytics dashboards.
Attribution Modeling
Multi-touch attribution with confidence scores. Probabilistic models that acknowledge data limitations.
Marketing AI Infrastructure
Personalization Engines
Real-time content and message personalization. Guardrails prevent off-brand or mis-targeted outputs.
Data Quality Monitoring
Automated detection of CRM drift, duplicate contacts, and data degradation. Issues flagged before they affect campaigns.
Production Observability
Request tracing, model performance monitoring, and cost tracking. Debug any production failure from logs alone.
Full-Stack AI Integration
Connect AI capabilities to your existing platform, APIs, databases, and workflows. Complete system integration and custom development.
B2B SaaS Platform Development
Complete platform development – interfaces, APIs, databases, authentication, integrations, billing, and other foundational features.
Infrastructure & Deployment
Production deployment, monitoring, scaling, CI/CD pipelines, and security implementation for AI systems and SaaS platforms.
"We've built our entire B2B SaaS platform together, and I genuinely can't imagine working with anyone else"
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
"Their transparency about AI capabilities has been crucial for making informed strategic decisions about our product."
— Kevin M.A. Nguyen, Co-Founder, Proximo AI
Here's how we work together:
Intro Call
We dig into your goals and challenges and figure out if we're the right fit. No sales pitch, but a honest conversation about what needs to happen.
"Softcery's approach is exceptionally thoughtful - they consider the complete business context, not just the immediate technical requirements."
— Kevin M.A. Nguyen, Co-Founder @ Proximo AI
Shaping Phase
We define exactly what gets built, document edge cases, and lock in timeline + budget. This is where we prevent the "6 months later..." nightmare.
"Softcery treated our project with the same care we would - validating every assumption and researching every angle before building."
— Charley Cohen, Director @ TIC.uk, Founder @ Ticitz
Build Phase
We deploy to production in the first weeks and iterate from there. Weekly updates, working functionality you can see and test immediately as we build.
"Softcery combines deep product strategy with technical execution - they don't just follow instructions, they challenge your approach and find better solutions."
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
Launch & Scaling
We finalize the production system, document everything properly, and either hand off cleanly or stick around as your ongoing AI partner. Your choice.
"Softcery was fantastic throughout our re-build of a recently acquired B2B SaaS. Highly recommend in you are in need of high quality AI and SaaS engineering."
— Chris Riley, Acme Studio (Cuppa AI, Bullseye, Experts Ink)
Ready to ship marketing AI that works with real customer data?
The AI Launch Plan covers the framework we use for marketing AI systems – testing strategies, personalization guardrails, and production patterns. Or schedule an intro call to discuss your specific requirements.
The Founder's Guide to AI Engineering
In-depth coverage of AI engineering for B2B SaaS founders. Analysis, technical breakdowns, and implementation guides from the field.
Agentic Systems
AI Agents Break the Same Six Ways. Here's How to Catch Them Early.
Works in staging. Fails for users. Six architectural patterns explain the gap, and all of them show warning signs you can catch early.
You Can't Fix What You Can't See: Production AI Agent Observability Guide
Failures you can't reproduce. Error logs that tell you nothing. Three observability pillars solve this: tracing, monitoring, and evaluation.
The AI Agent Prompt Engineering Trap: Diminishing Returns and Real Solutions
Founders burn weeks tweaking prompts when the real breakthrough requires a few hours of architectural work.
Choosing LLMs for AI Agents: Cost, Latency, Intelligence Tradeoffs
Demos work. Production reveals $47 conversations, 2-second pauses, unpredictable failures. Three dimensions help choose.
We Tested 14 AI Agent Frameworks. Here's How to Choose.
Your use case determines the framework. RAG, multi-agent, enterprise, or prototype? Here's how to match.
Voice Agents
How to Choose STT and TTS for Voice Agents: Latency, Accuracy, Cost
Every provider claims low latency and high accuracy. Real differences show up in production. Here's what actually matters.
Real-Time (S2S) vs Cascading (STT/TTS) Voice Agent Architecture
Both architectures work in demos. Different problems emerge in production. Here's what determines the right choice.
Choosing an LLM for Voice Agents: Speed, Accuracy, Cost
Fast models miss edge cases. Accurate models add 2 seconds. Cheap models can't handle complexity. Here's how to choose.
Why Voice Agents Sound Great in Demos but Fail in Production
Understanding why AI voice agents break down is the first step to building a solution that actually works in real life.
Testing Voice Agents: Methods, Metrics, and Tools
Controlled tests pass every time. Real users break agents with accents, noise, and bad networks. Here's what to test for.
Featured
Agentic Coding with Claude Code and Cursor: Context, Memory, Workflows
Agents go in circles without project context. The same agent ships production code daily with proper structure. Here's the system.
8 AI Observability Platforms Compared: Phoenix, Helicone, Langfuse, & More
AI agents fail randomly. Costs spike without warning. Debug logs show nothing useful. Eight platforms solve this differently.
US Voice AI Regulations: TCPA, BIPA, COPPA, HIPAA, & State Privacy Laws
Legal compliance sounds expensive and complex. Most voice AI startups need eight laws and a 5-step framework to ship safely.
11 Voice Agent Platforms Compared: Vapi, Ultravox, Retell, & More
Platforms promise easy setup. Production reveals control limits, concurrency caps, and cost scaling. Match your constraints before choosing.
SOC 2 for Voice AI Agents: Security, Confidentiality, and Quick Wins
Enterprise deals stall without SOC 2. Formal audits cost months and $50k+. Eight steps align your startup now before compliance blocks revenue.