OpenAI & Anthropic API Integration
Seamless integration with leading LLM providers including GPT-4, Claude 3, and Gemini. We handle authentication, rate limiting, and error handling so your team can focus on features.
AI & MACHINE LEARNING
LLM Integration Experts
We integrate GPT-4, Claude, Gemini, and open-source LLMs into your products and workflows — from simple API wrappers to production-grade RAG pipelines and fine-tuned custom models.
60+
LLM Integrations Delivered
GPT/Claude/Gemini
Model Expertise
3wk
Avg Integration Timeline
Fixed scope, fixed price
A number before we write code
Your IP from day one
Full ownership on final payment
Senior engineers only
No juniors on your budget
Reply within 1 working day
From an engineer, not a bot
Get Your Custom Project Plan
Share your project details — a senior engineer responds within one working day.
🔒NDA Protected
⚡24hr Response
💬Free Consultation
Our LLM Capabilities
RAG (Retrieval Augmented Generation)
Combine the power of LLMs with your proprietary data using vector databases and semantic search. Give your AI grounded, accurate answers based on your knowledge base.
Fine-tuning & Custom Models
Adapt foundation models to your specific domain and tone. We fine-tune models on your data to dramatically improve accuracy, reduce hallucinations, and lower per-token costs.
Prompt Engineering
Expert prompt design and optimization to maximize model performance. We develop systematic prompt frameworks, chain-of-thought strategies, and few-shot examples tailored to your use case.
LLM Observability & Monitoring
Full-stack monitoring of your LLM pipelines with latency tracking, token usage dashboards, output quality scoring, and alerting to catch regressions before they reach users.
Cost Optimization & Caching
Reduce LLM API spend by up to 80% through intelligent prompt caching, model routing, response memoization, and selecting the right-sized model for each task.
How we work
Fixed milestones, working software every fortnight, and no surprises on the invoice.
- 01
Use Case Scoping
We assess your goals, data, and existing systems to define the optimal LLM integration approach — from simple API calls to complex multi-agent pipelines.
- 02
Model Selection
We evaluate GPT-4, Claude, Gemini, Llama, and open-source alternatives across accuracy, cost, latency, and compliance requirements to recommend the best fit.
- 03
Integration & Testing
Full implementation with rigorous evaluation — red-teaming for safety, accuracy benchmarking on your data, and end-to-end integration testing in staging environments.
- 04
Production Deployment
We ship to production with CI/CD pipelines, rate limit handling, fallback strategies, and observability dashboards configured for long-term reliability.
Work we have shipped
Accountable partners, not outsourced vendors
How we price, who we staff and what happens after launch — the parts that decide whether a project actually lands.
Fixed scope, fixed price
You know the cost and the timeline before a line of code is written. No hourly billing, no scope-creep surprises.
You own everything
Full source code, credentials and intellectual property transfer to you on final payment. We sign the IP assignment on day one.
Senior engineers only
Every project is staffed by engineers with years of production experience — no juniors learning on your budget.
Canadian data residency
Built for PIPEDA and provincial privacy law, with data kept on Canadian infrastructure where it matters.
8-week MVP programme
A focused, fixed-price path from idea to a launched product, refined across dozens of startup engagements.
Support after launch
Tiered SLAs from monitoring and bug fixes to a dedicated team with 24/7 response. Most clients stay for years.
Recognition
Independent awards and partner tiers, each one earned on delivered work.
Top Generative AI Company
Top App Development Company
Webby Honoree
Best App Award, Personal Growth
Advanced Tier Services Partner
Top 100 North America
Compliance
Regulation is a design constraint here, accounted for from the first architectural decision rather than audited in at the end.
Privacy
Personal data handled to the letter of Canadian and international law.
PIPEDAGDPRHIPAACCPA
Security
Threat modelling, least-privilege access and audited controls.
SOC 2 Type IIISO 27001Zero trust
AI governance
Model evaluation, human oversight and an audit trail.
ISO 42001AIDA-readyModel cards
Sector
The regulations your industry answers to, designed in from day one.
PCI DSSHL7 / FHIROSFI
Cloud
Regional residency, encryption in transit and at rest, tenant isolation.
Canadian data residencyAWS Well-Architected
Accessibility
Built and tested so the people who need it can actually use it.
WCAG 2.2 AAAODA
Common questions
If your question is not here, a senior engineer will answer it within one working day.
Ask our teamIt depends on your specific needs. GPT-4 excels at complex reasoning and code generation. Claude is preferred for long-context tasks and safer outputs. Gemini is strong for multimodal applications. We benchmark all relevant models against your actual use case before recommending one — and often use multiple models in combination for best results.
Data privacy is configurable at multiple levels. Options include enterprise API tiers with zero data retention, private cloud deployments (Azure OpenAI, AWS Bedrock), and fully on-premise open-source models like Llama 3. We help you choose the right architecture based on your compliance requirements (GDPR, HIPAA, SOC 2).
Retrieval Augmented Generation (RAG) connects an LLM to your own knowledge base — documentation, databases, PDFs — so it can answer questions accurately using your proprietary data rather than just its training data. You need RAG when you want the AI to have up-to-date, domain-specific knowledge without the cost and complexity of full fine-tuning.
Costs vary widely by model and usage. GPT-4 Turbo runs around $0.01–0.03 per 1K tokens, while GPT-3.5 and Claude Haiku are 10–20x cheaper. For a typical customer support bot handling 10K queries/day, monthly costs typically range from $200–$2,000 depending on context length. We help you optimize architecture to minimize costs without sacrificing quality.
Yes. Fine-tuning is ideal when you need consistent tone, specialized domain vocabulary, or structured output formats. We support fine-tuning on OpenAI models (GPT-3.5, GPT-4o) and open-source models (Llama 3, Mistral). The process typically takes 2–4 weeks and includes data preparation, training, evaluation, and deployment to your infrastructure.
Let’s build something worth keeping.
Tell us what you are trying to build. A senior engineer will come back within one working day with a scope, a timeline and a fixed price.