🧠 Custom AI Development
Purpose-Built AI Systems Engineered for Your Business
When off-the-shelf tools hit a wall, we build what doesn't exist yet. Agents that handle multi-step decisions on their own. Models trained on your actual data. Systems that run your specific business logic — not a generic version of it. $50K–$250K+.
✓Fully custom — no off-the-shelf templates
✓Claude, GPT-4, and open-source LLMs
✓Private & HIPAA-aware deployment options
Live Example
Multi-Agent Pipeline in Action
This is what a custom agentic system looks like processing a new lead through a full sales qualification and onboarding pipeline — autonomously, in seconds.
1
Intake Agent receives lead data
Parses form submission, enriches with web data
2
Qualification Agent scores the lead
Evaluates fit, budget signals, urgency — routes accordingly
3
Proposal Agent drafts custom proposal
Pulls pricing rules, generates personalized PDF
4
Onboarding Agent activates client
Contract sent, portal created, CRM updated, team notified
multi-agent-pipeline · sales-automation
// 09:41:02 · new lead received · intake-agent
intake → parsing lead: "Sarah Chen · Law Firm · NYC"
// enriching with LinkedIn + web data
✓ company: 12 attorneys · $4M revenue est.
// handing off to qualification-agent
qualify → scoring lead on 14 criteria...
✓ score: 87/100 · tier: HOT · budget: $$$
// triggering proposal-agent
proposal → generating custom legal AI proposal
// pulling pricing rules · formatting PDF
✓ proposal ready · emailed to sarah@chenlegal.com
// onboarding-agent queued for reply
// Slack alert → sales team · 09:41:09
✓ pipeline complete · 7 seconds total
Custom AI Capabilities
6 Advanced AI Systems
We Engineer from Scratch
We pick the model and architecture that fits the problem. Not the other way around.
Domain-Specific Fine-Tuned Models
A generic model knows a little about everything. A fine-tuned model knows a lot about your specific domain. We take your data — past cases, patient records, transaction history — and train a model that outperforms general-purpose AI on your exact tasks.
- Custom training dataset curation
- Fine-tuning on OpenAI or open-source models
- Evaluation benchmarks and accuracy testing
- Continuous retraining pipeline
OpenAI Fine-TuningAnthropic ClaudeHuggingFaceAWS
Agentic AI Systems
An agent doesn't just run a script — it figures out what to do next, uses tools, checks its own work, and recovers when something goes wrong. We build these for tasks too complex or variable to handle with a fixed workflow.
- Reason → Plan → Execute → Feedback loops
- Tool use: web search, APIs, databases, code
- Human-in-the-loop checkpoints
- Observable and auditable decision trails
LangGraphAutoGenClaude APIn8nPython
Multi-Step Workflow Agents
The terminal demo above is a real example of this. A lead comes in, gets scored, gets a custom proposal generated, and kicks off onboarding — all without anyone on your team touching it. We map your process, find where decisions happen, and automate each one.
- Full business process mapping
- AI decision nodes at each stage
- Exception handling and escalation paths
- Real-time monitoring dashboard
n8nClaude APICustom PythonREST APIs
AI Infrastructure & API Integration
Connecting an AI API is easy. Building something that stays reliable at volume, stays cheap, and doesn't fall over when a model has an outage — that's the actual engineering work. We handle prompt design, cost monitoring, rate limits, and fallbacks so you don't have to think about it.
- Multi-model routing and fallbacks
- Cost monitoring and optimization
- Rate limiting and queue management
- Observability and logging
AWSDockerFastAPIClaude APIOpenAI
AI Analytics & Decision Intelligence
Most analytics tools tell you what already happened. We build systems that predict what's coming — which clients are about to churn, which leads are actually ready to close, where you'll need capacity next month — and trigger actions automatically when the threshold is crossed.
- Predictive churn and risk models
- Real-time decision automation
- Custom KPI dashboards with AI insights
- ROI attribution modeling
Pythonscikit-learnAWS SageMakerLooker Studio
Private AI Deployment
Some clients can't send patient data or case files through a third-party API — full stop. For those situations we deploy self-hosted models on your own infrastructure. You get the same AI capability, but the data never leaves your environment.
- Self-hosted LLMs (Ollama, vLLM)
- HIPAA and SOC2-aware architecture
- Air-gapped deployment options
- Zero third-party data exposure
AWS Private CloudDockerOllamaSelf-hosted LLMs
Investment
Custom AI Development
Pricing Tiers
Scoped to your exact requirements after a technical discovery call. These ranges reflect typical project complexity.
🚀
POC / MVP
$8K–$25K
3–6 week delivery
- ✓Proof-of-concept build
- ✓Single-agent workflow
- ✓Integration with 1–3 tools
- ✓60-day support
⚙️
Implementation
$25K–$100K
6–12 week delivery
- ✓Production-ready system
- ✓Multi-step workflow agents
- ✓Full API integration stack
- ✓6-month support
🧠
Advanced Agentic
$50K–$250K+
12–24 week delivery
- ✓Multi-agent orchestration
- ✓Fine-tuned domain models
- ✓Private deployment option
- ✓12-month support
🔁
Retainer
$2K–$8K/mo
Ongoing partnership
- ✓Continuous iteration
- ✓Model updates & retraining
- ✓Monitoring & optimization
- ✓Priority support SLA
FAQ
Common Questions About
Custom AI Development
Off-the-shelf tools work well for generic use cases. Custom development is the right choice when your workflow is unique, when you need proprietary data trained into the model, when off-the-shelf tools can't integrate with your stack, or when the cost of SaaS tools at scale exceeds a one-time build cost. We'll tell you honestly which direction makes sense after a discovery call.
A workflow agent follows a defined sequence of steps — it's deterministic and predictable. An agentic AI system can reason, adapt its plan, use tools autonomously, and self-correct when something goes wrong. Agentic systems are more powerful but require more careful architecture. We'll recommend the right approach based on your use case.
All of the above. We select the best model for each task — Claude for nuanced reasoning and long-context tasks, GPT-4 for broad capability, and open-source models (Llama, Mistral) for private deployments where data cannot leave your infrastructure. Many of our systems use multiple models in different stages of a pipeline.
We build HIPAA-aware and SOC2-aware architectures — using private cloud deployments, encrypted data pipelines, audit logging, and access controls. We recommend a compliance review with your legal and security team before go-live. For the strictest requirements, we offer fully air-gapped, on-premise deployments using self-hosted LLMs.
We build observable, auditable systems with human-in-the-loop checkpoints for high-stakes decisions. Every agent action is logged. We run extensive testing before go-live and implement confidence thresholds — below a certain confidence score, the system escalates to a human rather than acting autonomously. Monitoring dashboards alert us to anomalies in real time.
It starts with a 60-minute technical discovery call where we map your current processes, data sources, integration points, and success criteria. From that, we produce a technical scope document and fixed-price quote within 5 business days. No commitment required at the discovery stage.
🧠 Let's Architect Your AI System
Ready to Build Something That Doesn't Exist Yet?
Describe your use case and we'll tell you exactly how to build it — model selection, architecture, timeline, and fixed-price quote. No commitment required.
Book a Technical Discovery Call →