Models
Custom AI Software Development
Purpose-built AI applications designed specifically for your unique business challenges and workflows.
- open-weight
- you own the weights
- self-hostable
- SFT + LoRA
- Open
- Open-weight families
- Access the leading open-weight models from the Qwen, Kimi, and GLM families, fine-tuned on your data.
- ~30
- Days to first fine-tune
- From your data to a model running in production, then improved from real usage.
- Yours
- Weights + pipeline
- You own the trained weights, adapters, and the retraining pipeline. Self-hostable.
02 / The catalog
Open-weight models, fine-tuned and yours
One place for the models worth building on. Access the leading open-weight families, tune them to your data, and keep the weights.
- Qwen3.7-7B-InstructLanguageFast, low-cost base for chat, extraction, and classification.
- Qwen3.7-32B-InstructLanguageBalanced accuracy and cost for most production fine-tunes.
- Qwen3.7-72B-InstructLanguageFrontier accuracy for the hardest reasoning tasks.
- Qwen3.7-VL-7BVisionReads images, scans, and document layouts.
- Qwen3.7-VL-32BVisionHigher-fidelity visual understanding for inspection and OCR.
- KimiLanguageVery long context for whole-document and full-history reasoning.
- GLMLanguageStrong bilingual performance and tool use.
- GLM-VVisionVision-language model for multimodal workflows.
03 / Fine-tune
Configure a model, then watch it train
Pick the shape of the build and run an illustrative fine-tune. When it fits, book a build for that exact spec.
Spec the model, then watch it train.
Set the shape of the build and run an illustrative fine-tune right here: the loss falls, the eval climbs, and the log streams. Every number is an estimate, not a promise.
Compute band: ~1 to 2 GPU-h. Illustrative: params x examples x 3 epochs.
base_model: Qwen3.7-7B-Instruct
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
sequence_len: 8192
micro_batch_size: 2
gradient_accumulation_steps: 4
num_epochs: 3
learning_rate: 0.0002
optimizer: adamw_bnb_8bit
datasets:
- path: ./data/your-dataset.jsonl
type: chat_template
val_set_size: 0.05Base: Qwen3.7-7B-Instruct. Open-weight, trained on your data, owned by you.
04 / What it changes
What the build is designed to do
- 01Get AI solutions perfectly tailored to your specific business challenges
- 02Connect directly to your existing systems and workflows
- 03Own your AI technology and the competitive advantage it creates
- 04Scale solutions as your business grows without platform limitations
- 05Receive ongoing support and continuous improvement from our development team
- 06Achieve capabilities that off-the-shelf tools simply cannot provide
05 / Goes further with
Build a larger AI system
Most strong rollouts combine a few services. These pair naturally with Custom AI Software Development.
- AI Data Entry AutomationEliminate manual data entry with AI that reads, understands, and inputs data across your systems automatically.
- AI Appointment SchedulingSmart scheduling that optimizes calendars, reduces no-shows, and lets customers book effortlessly.
- AI Content GenerationProduce high-quality marketing content, social posts, and business communications at scale with AI.
- AI Lead GenerationFind and convert more local customers with AI that identifies, engages, and qualifies prospects automatically.
- AI Employee Training SystemsAccelerate employee onboarding and skill development with personalized AI-powered training programs.
- AI Quality Control & InspectionEnsure consistent product and service quality with AI that inspects, measures, and reports in real-time.
06 / By market
Where we build Custom AI Software Development
The markets where Custom AI Software Development maps to the most local demand. Start with your city, or the one nearest you.
07 / Proof
Custom AI Software Development in the real world
Real builds where this service did the work. See the setup, the rollout, and the results.
- Automating document drafting and intake for a law firmAutomated client intake and first-draft document generation cut document turnaround by 70% and freed attorneys from repetitive drafting and data entry.
- Deflecting half the ticket queue for a SaaS companyAn AI support layer reading docs and account data deflected 54% of tickets, cut first-response time 60%, and let support scale with the customer base instead of headcount.
08 / FAQs
Custom AI Software Development questions
How much does custom AI development cost?
Custom AI development costs vary significantly based on project scope and complexity. Simple AI applications like a custom categorization model might start at $5,000-15,000. More complex projects like full AI-powered applications with multiple features typically range from $20,000-100,000+. We always start with a paid discovery phase ($2,000-5,000) that produces a detailed scope, timeline, and fixed-price quote before you commit to full development. This approach eliminates surprises and ensures alignment on expectations.
How long does custom AI development take?
Timeline depends on complexity, but most projects follow a predictable pattern: 1-2 weeks for discovery and planning, 2-4 weeks for proof of concept, and 4-12 weeks for full development and deployment. Simple AI integrations can be completed in as little as 3-4 weeks total, while complex applications may take 3-6 months. We use agile development practices with regular check-ins and deliverables, so you see progress throughout and can provide feedback that shapes the outcome.
Will I own the code and AI models you build?
Yes. All custom code, trained models, and documentation created for your project are your intellectual property. We provide complete source code, model weights, training data configurations, and deployment documentation. You are free to maintain, modify, or extend the software independently if you choose. We also offer ongoing maintenance and support plans for clients who prefer us to manage updates, monitoring, and continuous improvement.
What technologies and AI frameworks do you work with?
We work with the full spectrum of modern AI technologies including Python, TensorFlow, PyTorch, scikit-learn, and Hugging Face for machine learning; OpenAI, Anthropic, and open-source LLMs for language AI; various computer vision frameworks; and cloud platforms including AWS, Google Cloud, and Azure for deployment. We select technologies based on what best serves your specific use case, performance requirements, and long-term maintainability, not based on trends or preferences.
Turn Custom AI Software Development into something your team actually uses.
Name the work you want this to handle. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.