We build reliable and advanced
AI systems
Meet Softcery: We build AI systems that work in production. We know how to apply AI to real business problems — and we know how to make it reliable, scalable, and ready for customers.
You're here because something isn't working.
Demo → production gap?
Most AI solutions work great in demos, but turning them into reliable, production-ready systems requires complex engineering.
Deploying AI feels too risky?
Most AI tools look functional, but deploying them means risking data leaks, crashes, or critical issues that are hard to fix.
Random AI breakdowns?
The AI fails unpredictably, and nobody can figure out how to make it work reliably every time.
Off-the-shelf AI doesn't fit?
Generic AI tools don't match your workflows or your edge cases. You need something built for how your business actually works.
Basic AI hit its limits?
A basic AI integration kind of worked, but you need sophisticated capabilities that off-the-shelf tools can't deliver.
Need AI capabilities nobody offers?
Your situation requires genuinely difficult AI functionality, and you need a team that can figure it out.
We've shipped 20+ AI systems across legal, hospitality, marketing, e-commerce, and more. These problems aren't new to us.
Here's how we solve these problems:
Understanding the problem space before writing code
We map edge cases and build test datasets from real production scenarios, not clean synthetic data that breaks in production.
Architecture informed by 20+ production AI launches
We make architecture decisions based on 20+ production launches across legal tech, marketing automation, e-commerce, and CRM.
Early production deployment and beta testing
We deploy to production in the first weeks and launch to 5-10% of users to learn what actually breaks.
Real-time quality evaluation on every response
We validate every response against quality criteria and flag failures immediately before they reach customers.
Observability that surfaces problems before customers do
We implement request IDs, execution tracing, and structured logging so failures can be reproduced and debugged.
Automated end-to-end testing infrastructure
We build automated test suites that catch breaks on every code change instead of weeks later from customer complaints.
Most teams skip these steps and ship broken AI. We've built the systems to get it right the first time. See our case studies.
What we build.
Conversational AI
Text Agents
Chat-based AI agents with context, memory, and tool integration.
Voice Agents
Real-time voice AI with speech processing and conversation handling.
Hybrid Agents
Combined text and voice capabilities working together across channels.
Document & Data AI
Document Processing
Extract and transform data from PDFs, forms, and document workflows.
Data Analysis
Pattern recognition, insights generation, and automated data interpretation.
Content Generation
Automated content creation, summarization, and document generation.
Pre-Built AI Accelerators
Chat Agent Template
Battle-tested chat architecture based on 20+ production launches.
Voice Agent Template
Proven voice agent with telephony, speech processing, and production patterns.
Workflow Agent Template
Multi-step orchestration patterns with tool integration and error handling.
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
Something brought you here. Let's fix it.
We build AI systems that work in production, handle real customer complexity, and scale with your product. Schedule an intro call to discuss your specific requirements.
The Practical Guide to AI Engineering
In-depth coverage of AI engineering for businesses. 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.