Technology Services · Generative AI Development
Production-ready generative AI development services
We help businesses build and deploy generative AI systems that create real value, not just impressive demos. From internal knowledge assistants to automated content pipelines and synthetic data engines, we design, fine-tune, and ship generative AI solutions that are production-ready, compliant, and built to last.
What we build
Generative AI solutions we deliver.
From knowledge assistants to content pipelines and code generation — generative AI built to run in production, not just a demo.
Expert Team & Proven Experience
10+ years in the industry, with 500+ happy clients worldwide.
Text Generation & Content Pipelines
Generation systems built around your style guides, tone, and domain knowledge — producing marketing copy, product descriptions, and long-form reports with guardrails and human review before anything goes live.
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Internal Knowledge Assistants
Retrieval-augmented assistants that sit on top of your documentation, wikis, and policies — giving your team a conversational interface to institutional knowledge, with every answer cited, sourced, and scoped by role.
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Document & Report Automation
Compliance reports, client summaries, and financial statements follow predictable formats but consume hours of manual effort — we automate the draft and route it for review with full audit trails and version control.
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Code Generation & Developer Tooling
Code completion trained on your internal codebase, automated review assistants, documentation generators, and test scaffolding — built to fit your existing development environment, not replace your engineers’ judgment.
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Image & Video Generation Systems
Generation models fine-tuned on your brand assets so output reflects your brand from day one — product images, marketing visuals, design variation tools, and computer vision training data at scale.
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Our approach
We build for production, not proof of concept
A generative AI demo that impresses in a meeting is easy to build; one that performs reliably under real load, handles edge cases, and stays within compliance boundaries as requirements change is not. We mitigate hallucination and confidently-wrong outputs systematically — through retrieval-augmented generation, output classifiers, human-in-the-loop review for high-stakes outputs, and factual consistency checks — so the system is one your team can trust, not one they have to babysit. Generative AI is one part of our broader AI and ML practice; for projects centered specifically on large language models, our dedicated LLM development practice goes deeper on model selection and fine-tuning infrastructure.
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Our process
From first call to live product
We start with discovery — mapping your use cases, auditing your data assets for fine-tuning readiness, and defining success metrics; if generative AI isn’t the right fit, we’ll tell you. From there we design the architecture, select the right base model, and build a working prototype benchmarked against your metrics, with red-teaming to surface failure modes early. Fine-tuning, integration, and guardrail development follow in sprints, with bias checks and adversarial input testing running throughout, before a phased launch and 90 days of active monitoring and tuning.
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Governance & flexibility
Fine-tuned to your domain, not generic
Off-the-shelf models don’t know your products, terminology, or compliance requirements, so we fine-tune on your proprietary data and aren’t tied to any single model provider — recommending a foundation model API or a fine-tuned open-weight model based on what fits your data, budget, and deployment environment. Training data is handled with PII detection and redaction, and where real data is limited or too sensitive to use directly, we build synthetic data generation systems with privacy verification to keep sensitive information from being reverse-engineered from the output.
Book a discovery callTechnologies we work with
- Python
- PyTorch
- Hugging Face
- LangChain
- Stable Diffusion
- Pinecone
- MLflow
- AWS SageMaker
- Azure AI
- Docker & Kubernetes
Related services
Part of our AI development services.
One of 13 specialized practices under our AI & ML hub — explore the ones most relevant to what you’re building.
FAQ
Frequently asked questions
What we hear most often about generative AI projects — accuracy, data privacy, fine-tuning, and what happens after launch.
What kinds of generative AI applications do you build?
We build text generation and content pipelines, document and report automation systems, internal knowledge assistants, code generation tools, image and video generation systems, and synthetic data pipelines. Our work spans healthcare, retail, finance, professional services, and HR.
How do you prevent generative AI from producing inaccurate or harmful outputs?
We use retrieval-augmented generation to ground outputs in your verified content, output classifiers to flag problematic responses, human-in-the-loop workflows for high-stakes outputs, and factual consistency checks. Hallucination mitigation is treated as a core engineering requirement, not an optional extra.
Can you fine-tune a model on our proprietary data?
Yes. Fine-tuning on your domain-specific data is one of the most effective ways to get outputs that are accurate, on-brand, and relevant. We handle the full pipeline from data preparation through to training, evaluation, and deployment, with your data handled securely throughout.
How long does a generative AI project take?
Simpler tools like content generators or internal assistants typically take 6 to 12 weeks. More complex systems involving fine-tuning, multi-modal capabilities, or extensive compliance requirements take 3 to 7 months. We give you a precise timeline after the discovery phase.
How do you handle data privacy when training generative models?
All training data goes through PII detection and redaction before it is used. Data is stored in secure, access-controlled environments. We apply data minimization principles throughout and build with GDPR, HIPAA, and SOC 2 requirements in mind depending on your regulatory context.
Do you provide ongoing support after the system is launched?
Yes. We include 90 days of post-launch support as standard, covering monitoring, output quality tracking, and tuning. After that, optional retainers cover base model upgrades, retraining on new data, prompt library refinement, and safety layer updates as the model landscape evolves.
Ready to put generative AI to work?
An honest assessment of what’s possible, what it will take, and how to get there. No obligation.