Foundation Models
Commercial and open-weight language models chosen for accuracy, speed, cost, and where data may be processed.
Generative AI Development
Generative AI development grounded in your company knowledge
Cypherox delivers generative AI development services for chatbots, assistants, translation, content, and research tools that draw on your verified data. Each system is evaluated for accuracy before launch and monitored for output quality once real users depend on it.
Enterprise AI Engine Active
Production Core
Targeted Generative AI Capabilities
Explore modular AI architectures tailored to your business:
Chosen by companies that use generative AI daily
What Cypherox Offers
Generative AI development company for five business functions
Every capability below runs on foundation models connected to your approved content through retrieval, so answers and drafts stay tied to sources your team trusts. Choose one or combine several.
AI Chatbot Development
Chatbots that answer customer and employee questions from your documents, cite their sources, and pass difficult conversations to your team.
- Hybrid retrieval over your content
- Source citations in answers
- Handover to human agents
Virtual Assistant Services
AI assistants that draft emails, prepare meeting notes, and organize follow-ups inside the tools your teams already use.
- Drafting and summarizing
- Calendar and inbox support
- Works within your workspace tools
AI Language Translator
Translation that follows your glossary and domain terminology, built into document and content workflows rather than copied into separate tools.
- Custom glossaries and style rules
- Document and file translation
- Reviewer checks before publishing
AI Content Generator
Content drafted within your brand rules, from product descriptions to technical documentation, with approval steps before anything is published.
- Brand voice and tone controls
- Templates for recurring formats
- Approval workflow before publishing
Virtual Research Assistant
Research tools that search your document collections and approved sources, then summarize findings with citations your analysts can check.
- Search across large document sets
- Cited summaries and comparisons
- Answers traceable to sources
AI Chatbot Development
AI Chatbot Development Services Grounded in Hybrid RAG
Customers stop trusting a support chatbot after one confident wrong answer. We combine keyword and semantic search over your help center, policies, and product documents, so the chatbot finds the passage that answers the question and shows where it came from. When confidence is low, it hands the conversation to a person instead of guessing.
- Hybrid Retrieval: Combines keyword and semantic search to find the most relevant passage.
- Source Citations: Shows customers and staff which document each answer came from.
- RAG Chatbot Pipelines: Keeps the chatbot's knowledge current as your documents change.
- Confidence Thresholds: Routes uncertain questions to a person rather than guessing.
- Multi-Channel Deployment: Runs on your website, help center, and internal messaging tools.
- Conversation Analytics: Shows unanswered questions so your team can fill content gaps.
Virtual Assistant Services
AI Virtual Assistant Development for Everyday Team Workloads
Much of the working day goes into drafting replies, summarizing meetings, and chasing follow-ups. Our AI virtual assistant development focuses on these tasks, connecting assistants to your email, calendar, and document tools with permissions set per user. Assistants prepare the work; your people review and send it.
- Email Drafting: Prepares replies in your tone based on the thread and context.
- Meeting Summaries: Turns transcripts into decisions, owners, and next steps.
- Follow-Up Tracking: Reminds users of open commitments and drafts the follow-up.
- Document Q&A: Answers questions about files the user already has access to.
- Per-User Permissions: Respects existing access rights in your workspace tools.
- Review Before Sending: Keeps a person in charge of every outgoing message.
AI Language Translator
AI Translation Solutions That Respect Your Terminology
Generic translation tools handle everyday language well but often miss the terms your industry depends on. Our AI translation solutions for business apply your glossary, style rules, and previously approved translations, then route sensitive documents to a reviewer before release. Translation runs inside your document and content workflows, so files do not leave controlled systems.
- Custom Glossaries: Applies approved terms consistently across every language pair.
- Translation Memory: Reuses translations your reviewers already approved for repeated content.
- Format Preservation: Keeps layout, tables, and formatting intact in translated files.
- Domain Adaptation: Tunes output for legal, technical, or product language.
- Reviewer Workflow: Sends high-risk documents to a qualified reviewer before release.
- Workflow Integration: Translates directly within your CMS, DMS, or support tools.
AI Content Generator
AI Content Generation Solutions Governed by Your Brand
Producing product descriptions, help articles, and marketing variants at volume strains any content team. Our AI content generation solutions draft these formats from your product data and approved sources, following tone and terminology rules your team defines. Nothing goes live until an editor approves it.
- Brand Rules: Applies tone, terminology, and banned phrases to every draft.
- Template Library: Generates recurring formats such as product pages and release notes.
- Source Grounding: Builds drafts from your product data rather than general knowledge.
- Variant Generation: Produces versions for different channels, audiences, or lengths.
- Editorial Approval: Routes every draft to an editor before publishing.
- Version History: Records each draft, edit, and approval for later reference.
Virtual Research Assistant
AI Research Assistant Development for Cited Knowledge Synthesis
Analysts and specialists lose hours searching reports, contracts, and internal files for the few passages that matter. We build research assistants that search those collections, compare findings across documents, and write summaries in which every statement links back to its source. Your team checks the evidence instead of hunting for it.
- Multi-Source Search: Queries internal documents and approved external sources together.
- Cited Summaries: Links every statement in a summary to its source passage.
- Cross-Document Comparison: Highlights where documents agree, differ, or leave gaps.
- Structured Extraction: Pulls defined fields such as dates, parties, or figures into tables.
- Saved Research Threads: Keeps questions, sources, and findings together for later review.
- Access Controls: Shows each user only documents they are permitted to see.
Security and Safety
Generative AI built for data security, output safety, and scale
Generative systems handle sensitive content and speak on your behalf. These four safeguards shape how we deliver LLM application development, from data handling to the way outputs reach users.
- Private Deployment Options: Models can run in model providers' EU regions or in your own cloud, depending on how sensitive the data is.
- No Training on Your Data: We configure model access so your prompts and documents are not used to train provider models.
- Output Guardrails: Filters check responses for off-topic content, personal data, and unsupported claims before users see them.
- Model Choice and Cost Control: We select models per task and monitor latency and cost so usage stays within the budget you set.
How Cypherox Delivers GenAI
Our five-stage generative AI delivery lifecycle
Most generative AI problems start with unprepared content, not with the model. Our process audits your data first, then designs retrieval, tests outputs, and monitors quality after launch.
Discovery and Content Audit
We confirm the use case, review the documents and data the system will rely on, and agree how quality will be measured.
Architecture and Model Selection
We choose models, retrieval design, and hosting based on accuracy needs, data sensitivity, response time, and running cost.
Retrieval Setup and Tuning
We prepare, chunk, and index your content, then tune prompts and retrieval until answers meet the agreed quality bar.
Guardrails and Testing
We add output filters and fallback rules, then test against real questions, difficult cases, and attempts to misuse the system.
Deployment and Quality Monitoring
We release to users in stages and track answer quality, unanswered questions, latency, and cost, adjusting as content changes.
GenAI Technology
Generative AI tools and frameworks we build with
We pick models, retrieval tools, and hosting per project, weighing answer quality, data residency, and cost. Each choice is made during architecture and model selection.
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Orchestration Frameworks
Libraries that manage prompts, retrieval steps, and multi-step generation flows.
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Vector Databases
Stores that index your content as embeddings so retrieval finds relevant passages quickly.
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Infrastructure and LLMOps
Hosting, deployment, and monitoring tools that track output quality, latency, and cost in production.
GenAI Technology
Generative AI tools and frameworks we build with
We build on established foundation models, retrieval stores, and deployment platforms, choosing each per project and connecting them through monitored, access-controlled pipelines.
Foundation Models
- • OpenAI GPT Models
- • Anthropic Claude
- • Google Gemini
- • Meta Llama
- • Mistral Models
Orchestration Frameworks
- • LangChain
- • LlamaIndex
- • Haystack
- • DSPy
- • Semantic Kernel
Vector Databases
- • Pinecone
- • Qdrant
- • Weaviate
- • Milvus
- • PostgreSQL pgvector
Infrastructure and LLMOps
- • Amazon Bedrock
- • Azure AI Foundry
- • Google Vertex AI
- • vLLM
- • LangSmith
Business outcomes from generative AI in production
60%+
Customer questions resolved by chatbots without staff involvement
4x
Faster first drafts for recurring content formats
98%+
Glossary term accuracy in reviewed translations
<2s
Typical response time for grounded chatbot answers
What clients say about our generative AI work
Product, support, and content leaders on how generative AI moved from a pilot to a system their teams rely on every day.
"Hi, my name is Gabrielle, and I'd like to share my experience working with Cypherox Technologies on one of the mobile apps and websites I built with them. The team members at Cypherox Technologies were attentive, diligent, communicative and very, very bright. I had an amazing experience working with them and I highly recommend."
"I'm Britney and I'm the head of conversion optimization for Acadia, a digital marketing agency. I've had the pleasure of working with the Cypherox team for over two, going on three years now. They're fast, hardworking and really diligent developers. They're really reliable, skilled developers and have been a great partner and a pleasure to work with."
"Hey I'm Brian and I recently worked with Cypherox Technologies who developed an e-commerce platform. For me, working with Cypherox Technologies was truly an excellent experience. I recently collaborated with them to design and develop the custom e-commerce platform from the Groundhog and their expertise was evident from day one."
"Hi, I'm Becke. I've had the pleasure of working with Cypherox Technologies on a recent web development project. It was a custom, heavy CRM and the experience was seamless from start to finish. The product was delivered on time and he went the extra mile to ensure everything was done so that we understood the process. It was just, it was brilliant. It was really, really good service. So yeah, absolutely brilliant; I'd recommend it. Thank you so much. Thank you."
"I would highly recommend working with Cypherox Technologies if you need a custom CRM platform. They always exceed their clients' expectations at every stage of the project. Their team designs and builds fully customized ecommerce platforms from the ground up, approaching every project with remarkable skill and dedication."
"Hi, my name is Gabrielle, and I'd like to share my experience working with Cypherox Technologies on one of the mobile apps and websites I built with them. The team members at Cypherox Technologies were attentive, diligent, communicative and very, very bright. I had an amazing experience working with them and I highly recommend."
"I'm Britney and I'm the head of conversion optimization for Acadia, a digital marketing agency. I've had the pleasure of working with the Cypherox team for over two, going on three years now. They're fast, hardworking and really diligent developers. They're really reliable, skilled developers and have been a great partner and a pleasure to work with."
"Hey I'm Brian and I recently worked with Cypherox Technologies who developed an e-commerce platform. For me, working with Cypherox Technologies was truly an excellent experience. I recently collaborated with them to design and develop the custom e-commerce platform from the Groundhog and their expertise was evident from day one."
"Hi, I'm Becke. I've had the pleasure of working with Cypherox Technologies on a recent web development project. It was a custom, heavy CRM and the experience was seamless from start to finish. The product was delivered on time and he went the extra mile to ensure everything was done so that we understood the process. It was just, it was brilliant. It was really, really good service. So yeah, absolutely brilliant; I'd recommend it. Thank you so much. Thank you."
"I would highly recommend working with Cypherox Technologies if you need a custom CRM platform. They always exceed their clients' expectations at every stage of the project. Their team designs and builds fully customized ecommerce platforms from the ground up, approaching every project with remarkable skill and dedication."
Frequently Asked Questions
Answers for product, data, and IT leaders comparing generative AI development services, covering accuracy, data use, hosting, and how each capability fits your existing systems.
Not sure which capability fits?
Describe the problem you want generative AI to solve, and we will suggest the capability and setup that suits it. Need a broader roadmap? See AI strategy consulting.
Discuss Your Use Case
What do generative AI development services include?
They cover use-case selection, preparing your content for retrieval, choosing models, building the application, adding guardrails, testing output quality, and monitoring after launch. The aim is a system that answers or drafts from your verified data, not a general-purpose demo.
What is the difference between generative AI and AI agents?
Generative AI produces answers, drafts, translations, and summaries for people to use. AI agents go further and take actions in your systems, such as updating records. If your use case needs the system to act, see our AI agent development services.
How does RAG reduce wrong answers?
Retrieval-augmented generation finds relevant passages in your documents and gives them to the model before it responds. The model answers from that material instead of relying on general training data, and each answer can cite its source for checking. Our RAG development services include tuning this retrieval step.
Will our data be used to train AI models?
No. We configure model access so your prompts, documents, and outputs are excluded from provider training, and we can use self-hosted models where your policies require it. Data handling terms are confirmed in writing before any project data is shared.
Where are generative AI systems hosted?
Hosting depends on data sensitivity and your GDPR obligations. Options include model providers' EU regions, your own cloud account, or self-hosted open-weight models. We settle hosting during architecture and model selection, because it affects which models are available.
How is an AI chatbot different from a virtual assistant?
A chatbot answers questions for customers or staff from approved content. A virtual assistant works for an individual user, drafting emails, summarizing meetings, and tracking follow-ups inside their own tools and with their own access rights.
Does the EU AI Act apply to generative AI systems?
Yes, in several ways. Chatbots must tell users they are interacting with AI, and some AI-generated content must be identifiable as such. We build these transparency measures into the system, while your legal team confirms which obligations apply to your use case.
How much do generative AI development services cost?
Cost depends on the capability, the volume and condition of your content, the number of integrations, quality and review requirements, and hosting choice. Model usage is an ongoing cost that grows with traffic. We provide a scoped estimate after the discovery and content audit.
Start With One Use Case
Let's plan your GenAI system
Tell us which questions, documents, or content tasks you want generative AI to handle. We will review your content, suggest an architecture, and outline what a first release could include.
- Review your use case and content sources
- Get an architecture and model recommendation
- Receive a scoped proposal and timeline