18 Weeks
From initial concept to your first paying tenant, we build, launch, and refine your product for real-world success.
AI SAAS Products, Built To
Survive Production
We design and ship multi-tenant AI software retrieval, agents, scoring and copilots with the evaluation harnesses, guardrails and billing plumbing that turn a convincing demo into a product people pay for.
Build Your SaaS Product With an Experienced AI Development Team
Work with AI engineers, SaaS architects, product designers, cloud specialists, and QA experts dedicated to building scalable software that delivers meaningful business value.
99.9% Uptime Target
We engineer reliable AI systems with a 99.9% uptime target, ensuring consistent performance and dependable business operations.
12 AI System In Production
12 AI systems deployed in production, delivering reliable, scalable, real-world business value.
SOC 2 Ready Architecture
Build secure, scalable systems with SOC 2-ready architecture, designed for stronger controls, compliance, data protection, and enterprise requirements.
What You Get
What AI SaaS Development includes
Each one is a standing practice with its own checklists, reference architectures and review gates.
Multi Tenant Architecture
Tenant isolation, per-seat and usage-based billing, and data boundaries settled before the first feature is written.
Retrieval & Knowledge
Chunking, embeddings, hybrid search and citation-backed answers over your own corpus.
Agentic Workflows
Tool-calling agents with deterministic fallbacks, retries and human approval gates on anything irreversible.
Evaluation Harnesses
Golden sets, regression suites and offline scoring so a model or prompt change never ships blind.
Guardrails & Safety
Prompt-injection defence, PII redaction, rate limits and a full audit trail on every model call.
LLMOps & Cost Control
Model routing, caching, token budgets and per tenant cost dashboards from day one.
How We Work
Four phases, agreed before we start.
The same delivery rhythm on every engagement, so you always know what happens next.
Step 01
Feasibility & Data Audit
Two weeks on your data and the task. We tell you what the model can actually do before you fund a build.
Step 02
Architecture & Eval Baseline
Tenancy model, retrieval design and a scored golden set that defines what “good” means.
Step 03
Build & Iterate
Two-week increments. Every release is measured against the baseline, not against a demo.
Step 04
Launch & Operate
Cost dashboards, drift monitoring, run books and a 90-day hypercare window.
Tooling
The Stack Behind This Service
Explore the modern technologies, frameworks, and tools we use to build scalable, secure, production-ready AI solutions.
Models and Protocols
- Claude API
- OpenAI function calling
- MCP
- Deepgram Voice Agent
Build
- Python
- Fast API
- MCP
- React
- TypeScript
- PostgreSQL
Run
- AWS
- Docker
- Sentry
- GitLab CI
- n8n
- Make.com
Integration Shipped
- Twilio Voice
- Twilio WhatsApp
- ClickUp
- Notion
- Fireflies.ai
- SendGrid
Engagement Models
Three Ways To Start
Pick the shape that matches how defined the work already is.
01
AI Product Discovery
A fixed-price sprint that ends with a scored feasibility report, architecture and delivery plan.
- 2-3 Weeks
- Fixed price
- You own the output
02
End - To - End Build
A dedicated team takes your AI SaaS from empty repository to paying customers.
- Squad of 4-6
- Monthly retainer
- Weekly demos
03
Platform Hardening
We take an AI prototype that works and make it something you can sell and support.
- Evals + Guardrails
- Cost controls
- Load tested
The Hardest Part
Defining Where the Model Must Not Make Decisions
Give a model twelve overlapping tools, and it starts guessing. Fewer, clearly defined tools with distinct purposes deliver better results.
Each tool gets one clear purpose, tightly scoped arguments, and only the permissions required. Irreversible actions require human approval, with these boundaries defined early to avoid costly agent rewrites.
Each tool has one defined purpose, backed by a strict schema that blocks invalid arguments before they ever reach your database.
Clear limits on steps, spending, and time that trigger escalation instead of allowing the agent to loop endlessly.
Every tool call is logged with its inputs and results, making past runs fully traceable, reviewable, and replayable whenever needed.
Irreversible actions always require human approval before execution, without exception or compromise.
FAQs
Questions we get about this service
Find clear answers to common questions about our services, process, pricing, technology, timelines, and ongoing support.
Contact UsAlmost never. We start with hosted models and a strong retrieval layer, and only move to fine-tuning or self-hosting when the evaluation data says it earns its cost.
Have An AI Idea That Needs To Become A Product?
Send us the problem and the data you have. We will tell you honestly whether a model is the right answer.