Generative AI development services

Enhance decision-making, streamline operations, and adapt effortlessly to fast-changing market conditions with state-of-the-art Generative AI systems

Other expertises:

ML & AI development AI chatbot development

What we offer

We are here to deliver end-to-end generative AI solutions tailored to your industry, infrastructure, and strategic roadmap. Our portfolio includes AI-powered DevOps assistants, knowledge automation tools, intelligent chatbots, and custom LLM-based agents that support decision-making in highly regulated sectors. We have successfully integrated generative AI into both greenfield and legacy systems, ensuring scalability, security, and compliance.

Our offerings cover the entire development lifecycle:

– Ideation and rapid prototyping with tools like Flowise and n8n.
– Data strategy and knowledge pipeline implementation.
– Fine-tuning and deployment of open-source or commercial LLMs.
– Cloud-native and on-premise deployment (AWS, Azure, GCP, Kubernetes).
– Ongoing model evaluation, monitoring, and cost optimization.

Whether you’re considering AI to enhance existing workflows or pioneer new AI-driven capabilities, we bring the engineering expertise and domain knowledge to create a solution not just technically sound—it brings measurable value and is built for long-term profitability.

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Our team is ready to uplevel your Generative AI performance, functionality, and usability.

Our generative AI development services

Generative AI strategy and consulting

With our consulting services, we help you define your strategy, evaluate feasibility, and identify high-impact use cases. We assess data readiness and support you in data-related challenges—whether that means consolidating fragmented sources, filling critical gaps, or improving data quality. We select the most suitable models (e.g., GPT, Claude, Mistral, LLaMA) and design scalable solution architectures. Our team guides you through every technical and business decision, from compliance considerations to cost optimisation.
Generative AI development services

We build custom AI systems tailored to your use case—from content generation and summarisation to intelligent assistants and retrieval-augmented generation (RAG). Our team handles model orchestration, backend logic, data pipelines, and infrastructure deployment. Whether hosted via commercial APIs or fine-tuned on private infrastructure, we deliver secure, high-performance solutions ready for production.
Data engineering for generative AI

We design and implement data pipelines optimised for generative model training, evaluation, and inference. This process includes structured data collection, transformation, embedding, and storage using tools like Supabase, PostgreSQL, and vector databases (e.g., Pinecone, Qdrant). For RAG systems, we ensure efficient chunking, indexing, and semantic search performance across large knowledge bases.
Solutions customized to your business

We work with an understanding of each business’ uniqueness. Therefore, we tailor model choice, prompt strategies, infrastructure, and workflows to your specific domain and goals. Whether you’re in healthcare, legaltech, biotech, or SaaS, we develop systems that seamlessly integrate into your environment, deliver business-relevant results, and remain adaptable as your needs evolve.
Advanced AI solutions to solve complex problems

For clients with complex requirements, we offer advanced generative AI capabilities, including multi-agent orchestration, fine-tuned LLMs, low-rank adaptation (LoRA), hybrid cloud deployments, and secure on-premise solutions. We also build systems with embedded governance tools, red-teaming protocols, and safety layers to meet the highest standards for enterprise and regulated sectors.
Turning prototypes into production-ready AI systems

We take your idea from a quick proof-of-concept to a reliable, scalable, and monitored production system. Starting with no/low-code tools (like Flowise or n8n) for early validation, we then implement backend services, prompt logic, vector storage, and inference APIs to ensure seamless handover to production—with CI/CD and observability built-in.
Generative AI integration for businesses

We integrate generative AI into your existing products, platforms, and internal tools. This might include embedding LLMs into SaaS workflows, building agentic RAG systems on top of your document repositories, or enabling AI-driven customer support—all designed for secure access control, scalable API integration, and frictionless deployment. We ensure every integration is timely, stable, and non-disruptive to your core operations.
Automating business workflows with AI

We develop AI agents that automate repetitive, time-consuming tasks across departments—marketing, operations, HR, customer service, and more. Using LLMs and orchestration tools, we build systems that execute multi-step processes, route data, respond to queries, and trigger actions across tools—improving speed, accuracy, and resource allocation, and serving as reliable and efficient AI assistants.
Empowering smart devices with generative AI

We extend generative AI capabilities to edge and embedded environments, enabling intelligent features in smart devices. We design AI solutions that run efficiently within device constraints, from voice-enabled assistants and on-device summarisation to offline LLM inference using quantised models— all for supporting real-time interaction, automation, and low-latency intelligence at the edge.

Benefits of generative AI development services

State-of-the-art technologies

We build generative AI systems using the most advanced models and frameworks available—GPT-4, Claude, Mistral, LLaMA 2, and other cutting-edge architectures. Our team actively monitors the evolving AI landscape, ensuring your solution leverages the latest trends in transformer-based models, retrieval-augmented generation (RAG), low-rank fine-tuning (LoRA/QLoRA), and token-efficient inference strategies. This keeps your business competitive, performant, scalable, and future-ready.
Customised solutions for your specific needs

As it’s been said, we don’t believe in one-size-fits-all AI. Every solution is engineered around your unique data, workflows, and performance requirements. Whether we are talking about a domain-specific chatbot, a legal summarization engine, or an internal document assistant—we adapt model selection, prompt design, data strategy, and deployment architecture to your specific use case. The result is a system that delivers measurable value—not just generic output.
A seamless, end-to-end development process

Blackthorn Vision’s full-cycle team includes AI/ML engineers, backend developers, data pipeline architects, DevOps, and product managers. We manage every layer of development—from ideation and prototyping to deployment, monitoring, and post-launch tuning. Such an integrated approach eliminates hand-off delays and quality gaps, bringing consistent progress and allowing for quick pivots and production-grade delivery without friction.
Solutions for evolving business needs

As your business grows—whether through higher data volume, increased concurrent users, or feature expansion—our architecture adapts without compromise. We leverage containerized microservices, stateless APIs, and horizontal scaling via orchestration platforms like Kubernetes to ensure consistent performance and no disruptions. We design for flexibility and build scalability into every layer of the systems so that they scale seamlessly across cloud-native, hybrid, or on-premise infrastructures.
Optimised costs through intelligent AI automation

We confidently balance both performance and cost-efficiency. By automating repetitive workflows and enhancing decision-making through intelligent generation, we reduce dependency on manual input. We implement token-aware prompting, batch inference, selective model routing (e.g., switching between local and commercial APIs), and smart caching to minimize compute costs. This strategic resource optimization lowers operational expenses while maximizing system impact and long-term ROI.
Empowered creativity and productivity

Generative AI incredibly enhances teams’ productivity by streamlining and accelerating content generation and research and ideation processes across roles and departments. This applies to generating marketing copy and technical documentation as well as answering domain-specific queries or designing UI components. By offloading repetitive, low-value tasks, teams can shift their focus toward strategic initiatives that require human attention, significantly accelerating innovation cycles and introducing new creative potential.

Generative AI development process

01

Initial consultation and vision alignment

We begin with a structured discovery session to surface your business goals, operational challenges, and user expectations. Our AI specialists then assess whether generative models—such as GPT, Claude, Mistral, or other open-source alternatives—are suitable for your specific use case, whether that’s summarisation, process automation, virtual assistants, or agentic RAG systems.

We define key technical requirements, data readiness and constraints, and success criteria such as accuracy, latency, or user engagement. From there, we design a tailored solution architecture and delivery roadmap, detailing model options (API-based vs. self-hosted), integration pathways, and compliance needs, including data governance and security practices.
02

Rapid prototyping for ideas validation

In this phase, we quickly validate the solution concept using no-code/low-code tools such as n8n, Flowise, or Voiceflow.

We create interactive POCs that simulate real user interactions and system responses. This may include connecting LLMs (via OpenAI, Hugging Face, or custom APIs) to external tools like Notion, Slack, CRMs, or internal knowledge bases. Using tools like Flowise, we visually design prompt chains, memory components, and conditional logic to reflect the target functionality. For voice-enabled applications, Voiceflow allows fast prototyping of multimodal experiences such as AI agents, IVR flows, or Alexa-like assistants.

This step provides early feedback on the feasibility, UX flow, and alignment of the LLM responses with real user needs—critical for refining prompts, inputs, and workflows before committing to full development.
03

Data collection, cleaning, and preparation

We collect and prepare training and inference data using structured pipelines. This includes public domain datasets, internal knowledge bases, or synthetic data generation, depending on the use case.

We use Supabase (a PostgreSQL-based platform) for storing chat history, context documents for RAG, and metadata. Its built-in APIs and real-time capabilities simplify storage and retrieval. Schema design is minimal and scalable—supporting session tracking, embeddings, user feedback, and prompt logs.

Text data is tokenized, normalized, and structured for model consumption. For RAG, we preprocess and chunk documents, then embed them using vectorizers (e.g., OpenAI, SentenceTransformers) for semantic search.
04

Designing and developing tailored AI solutions

We architect the full solution, selecting the appropriate foundational model, orchestration framework, and infrastructure layer. Depending on the use case, we may use:
– API-based models (OpenAI, Anthropic)
– Self-hosted models (LLaMA 2, Mistral, Mixtral)
– Retrieval-augmented generation (RAG) frameworks
– Custom agents (LangChain, LlamaIndex)
The development includes backend API setup, embedding pipelines, prompt chaining logic, and governance controls (e.g., redaction, moderation, feedback collection). Vector search tools (e.g., Pinecone, Weaviate, Qdrant) are integrated for fast, semantically relevant content retrieval.

Security and explainability are prioritized through content filters, audit trails, and optional user identity linking (OAuth, JWT).
05

Model training and domain-specific fine-tuning

We select the most efficient adaptation method based on your performance and budget requirements:
– Prompt engineering for zero/few-shot solutions using commercial APIs
– Instruction tuning for task-specific adjustments
– Fine-tuning full or distilled open-source models (e.g., LLaMA 2, Mistral)
– LoRA/QLoRA for parameter-efficient training with reduced compute cost
Data is split into training, validation, and test sets. Fine-tuning occurs on secure GPU/TPU infrastructure with version control and regular checkpointing. Tools like Hugging Face Transformers, PEFT, or Axolotl are used to streamline experimentation.

For RLHF or alignment tasks, we use human feedback pipelines to enforce tone, compliance, or brand-specific behaviour.
06

Comprehensive testing and model evaluation

We implement multi-layer evaluation across performance, robustness, safety, and factuality. Automated tests include perplexity, BLEU, ROUGE, and retrieval accuracy. Human-in-the-loop evaluation assesses output coherence, tone, and business relevance.

For RAG-based systems, we test retrieval accuracy, chunk relevance, and hallucination rate using purpose-built datasets. Safety mechanisms—like prompt injection resistance, prompt filtering, and response moderation—are stress-tested with adversarial prompts.

Evaluation frameworks may include LangChain Evaluators, OpenAI Evals, or custom test harnesses for regression testing.
07

Deployment and post-launch monitoring

We deploy the solution in a containerized environment using Docker and Kubernetes, supported by CI/CD pipelines (e.g., GitHub Actions, GitLab CI) and IaC (Terraform). Hosting options include AWS, GCP, Azure, or on-premise environments.

For inference, we expose REST or GraphQL APIs with secure access layers (rate limiting, auth tokens). If RAG is used, vector DBs (Pinecone, Qdrant) are monitored for drift and latency.

Monitoring tools like Langfuse, Prometheus, or OpenTelemetry track model usage, token consumption, failure rates, and prompt variations. Feedback loops are built into the interface to continuously refine responses. Drift detection and retraining triggers are implemented for long-term reliability.

Generative AI tech stack

Python

Docker

Numpy

Pandas

TensorFlow

Matplotlib

Plotly

OpenCV

Kubernetes

Azure ML

Vertex AI

AutoML

Lama

Postgre SQL

Need to develop your generative AI ?

If you’re ready to transform how your business creates value, we are here to help make it real with our generative AI development services.

Daryna Chorna

Customer success manager

What you achieve with our generative AI development services

Streamlined content creation

Generative AI automates content production by leveraging large language models (LLMs) and multimodal transformers to generate high-quality text, visuals, audio, and video. Blackthorn Vision enables enterprises to scale content creation for blogs, product descriptions, ads, social media, and customer messages—while preserving consistency and brand integrity. We fine-tune the models to industry-specific datasets, ensuring relevance, tone, and compliance with regulatory standards where required.
Enhanced document intelligence

We apply natural language processing (NLP) and generative summarization techniques to automate complex document workflows. We also enable extraction of structured data from unstructured formats (e.g., PDFs, scanned documents, emails), automatic document classification, and generation of concise summaries. This enhances the accuracy and speed of contract analysis, invoice processing, and compliance auditing—reducing turnaround time and operational overhead.
Process optimization and workflows automation

Generative AI facilitates the automation of routine operations, including internal documentation, report generation, and decision-making processes. We build custom solutions that integrate with enterprise resource planning (ERP) and customer relationship management (CRM) systems to automate data flows, predict outcomes, and generate recommendations. This results in improved task allocation, error reduction, and increased overall productivity.
Faster time to market

By applying generative design models, simulation tools, and reinforcement learning, we help clients reduce the time from concept to launch, and enter the market much faster. AI assists in ideation, automates UI/UX prototyping, and accelerates testing through synthetic data generation and model-based validation. Our development frameworks support iterative refinement, allowing teams to explore more design variants and optimize features based on performance predictions and user data.
Automated support with chatbots and AI assistants

Blackthorn Vision develops intelligent chatbot agents powered by LLMs and retrieval-augmented generation (RAG) systems. These bots handle dynamic, multi-turn conversations, draw on live knowledge bases, and learn from ongoing user interactions. Whether deployed for technical support, HR inquiries, or sales, these virtual assistants reduce service response times, improve query resolution rates, and offer 24/7 multilingual support with enterprise-grade security.
Smarter design and insight-led development

We develop AI systems that analyse large datasets, generate predictive insights, and visualise trends in real time. Using advanced techniques like generative analytics and anomaly detection, we support strategic planning, customer segmentation, and demand forecasting. These tools serve precision-driven decisions, reduce reliance on historical intuition, and pinpoint hidden patterns essential for innovation and growth.

What our clients say

4.8

Berkeley Lights

VP of Software

USA

“Blackthorn Vision has been involved from the beginning. They’ve done almost all the software development on this product. Their professionalism distinguishes them. Blackthorn Vision’s teammates are good listeners and good workers.”
ANC

Chief technology officer

USA

“They work to help develop our company instead of only being a third-party service provider. As a result, they've become a part of our company, which is very cool. Blackthorn Vision has shown that they're willing to go beyond the call of duty to do their job.”
Sensia

Digital Architect, Web-Based IoT Platform

USA

“The quality of the work and engagement has been so good. They go beyond simply executing a task, story or test and are genuinely interested in understanding what the end user wants/needs.”
Index.dev

Director of Technical Recruitment & AMD Team

UK

“One of the most impressive facts about Blackthorn is that they are very sustainable and stable partner. Good communication, good dedication for their job, and taking a lot of responsibility on their project.”
Selux Diagnostics

Senior Program Manager

USA

“Blackthorn resources are embedded in our team and serve as an extension to our workforce. And during the inevitable crunch periods Blackthorn was able to rapidly increase our access to a skilled resource pool on a temporary basis to meet important milestones.”
Base Body

General Manager

Australia

"The range of skillsets in this company with the various employees, attention to detail and professionalism is impressive. To every problem was a good solution."
Balanced Flow

Vice President

USA

“This company clearly is dedicated to customer satisfaction. They volunteered improved approaches and modifications to requirements we never would have thought of. Their initiatives made the product much better. Their professionalism exceeded our expectations.”
BrightArch

BrightArch

Norway

“They’re reliable and deliver what they promise. Building an attractive work environment allows them to hire great developers. They’ve had a lot of great suggestions from the start.”
Townhill Software

Head of Product

Canada

“The most impressive thing about Blackthorn Vision was the dedication of the team to deliver on assigned milestones without clear indications. Although I possessed significant knowledge about the topic, I was mistaken about how much more there is to learn.”
CostDraw

Director of project Services

USA

“They’re technically very competent. They know exactly what they’re doing. I’d wholeheartedly recommend Blackthorn Vision.”
SiTime Corporation

Director of Customer Engineering

USA

“Blackthorn Vision LLC had a very professional demeanor and drove all tasks to completion. They did what they said they would do and did it on time like clockwork.”
RemiPeople

Chief Executive Officer

Australia

“In addition to their technical skill, the team is responsive and have been a real partner throughout the process.”
Orderica.com

Chief Executive Officer

USA

“I appreciate their loyalty. Blackthorn was always trying to resolve my problems. They did what I was expecting them to do; they’re great implementers.”
Orderica.com

Chief Executive Officer

USA

What makes us a great choice for generative AI development?

01
Up-to-date, expert engineering

Our cross-functional teams combine deep expertise in AI/ML, backend engineering, DevOps, and secure architecture design. We handle the entire AI lifecycle—from prompt engineering and fine-tuning to full-scale deployment with RAG, vector databases, and inference optimization. Our engineers build scalable, production-grade systems using best practices in API design, CI/CD, and container orchestration. We emphasize modularity, observability, and maintainability to ensure your generative AI product can evolve with your business.

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02
Adaptive tech stack expertise

We work with both proprietary and open-source technologies to meet performance, privacy, and cost requirements.
– LLMs & APIs: OpenAI API, Claude, Mistral, LLaMA, Mixtral, Hugging Face API and models
– RAG & Orchestration: LangChain, LangGraph, LlamaIndex, Haystack
– Embeddings: SentenceTransformers
– Vector Databases: Pinecone, Weaviate, Qdrant
– Storage & Backends: Supabase, PostgreSQL, Firebase, Redis
– Infrastructure: Docker, Kubernetes, AWS/GCP/Azure, Terraform
– Monitoring & Evaluation: Langfuse, Langsmith, OpenAI Evals, Prometheus, Grafana
– Prototyping tools: Flowise, n8n, Voiceflow

This flexible, production-ready stack allows us to move quickly from idea to deployment while maintaining the highest level of performance and reliability. We adapt to your needs and requirements and remain creative to offer the best possible solution for your idea or problem.

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03
Agile development process

We follow an agile, test-driven development model tailored for generative systems. Prototyping begins with quick validation using no/low-code tools, followed by structured data pipelines and modular backend design. Models are tested in isolated stages—prompt tuning, RAG integration, and model behaviour refinement—before full deployment. Each iteration is informed by usage analytics, human feedback, and safety evaluations. Thanks to this approach, we ensure the solution remains aligned with business goals, end-user needs, and model governance standards throughout the lifecycle.

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04
Sector-specific experience

Our generative AI experience spans healthcare, legal-tech, marketing, customer service, and other sectors. We’ve built AI copilots, search agents, content generators, questionnaires, and internal knowledge tools—always aligned with domain-specific compliance, privacy, and performance requirements. This cross-sector exposure allows us to bring tested strategies, reusable components, and proven design patterns to every engagement, reducing both technical and business risk.

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