Presear builds enterprise generative AI applications — LLM fine-tuning, RAG pipelines, image/video generation, code generation, and multi-modal AI — deployed securely on your infrastructure.
Technical Depth
From fine-tuned LLMs to production RAG pipelines — we match the right generative AI technique to your enterprise use case.
Adapting large language models to your domain and tone through supervised fine-tuning, instruction tuning, and reinforcement learning from human feedback. We use parameter-efficient methods (LoRA, QLoRA) to cut compute costs while achieving state-of-the-art domain accuracy — without leaking your data to third-party APIs.
Building knowledge-grounded AI systems that retrieve relevant context from your document corpus before generating answers — dramatically reducing hallucination and keeping responses factually anchored to your enterprise data. We design chunking strategies, embedding pipelines, rerankers, and hybrid search for production-grade accuracy.
Deploying and fine-tuning diffusion-based image generation systems — Stable Diffusion, ControlNet, and custom LoRA adaptations — for brand-consistent visual content, product imagery, synthetic training data generation, and creative workflows. We handle both cloud-hosted and on-premise deployments with safety filters.
Building systems that reason across images, documents, and text simultaneously — enabling document understanding, visual question answering, image captioning, and product analysis at scale. We work with vision-language models (VLMs) including GPT-4V, LLaVA, and Idefics for enterprise document and media pipelines.
Deploying code-specialized LLMs for test generation, code review automation, legacy migration, API documentation, and developer productivity tools — integrated into your CI/CD pipeline or IDE. We fine-tune on your codebase to produce context-aware suggestions aligned to your team's conventions and patterns.
Systematically designing, testing, and optimizing prompt pipelines — including chain-of-thought reasoning, few-shot exemplars, constitutional AI constraints, and agentic tool-use patterns — to maximize reliability and accuracy without fine-tuning. We build prompt management systems with version control and A/B evaluation frameworks.
Our Process
A structured five-stage process for building safe, accurate, and scalable generative AI. Click any step to explore.
We start by mapping the exact generative AI opportunity to measurable business outcomes — defining what gets generated, for whom, and under what constraints. This scoping prevents over-engineering and ensures every subsequent decision is anchored to business value rather than technical novelty.
The quality of generative AI output is bounded by the quality of the data it learns from. We collect, clean, deduplicate, and structure your enterprise data — documents, logs, code, customer interactions — into training and retrieval corpora with PII scrubbing, format normalization, and quality scoring baked in.
We select the optimal base model — open-source or proprietary — and apply the minimum necessary adaptation: from zero-shot prompting to full fine-tuning with RLHF, depending on the accuracy gap. Every experiment is benchmarked against domain-specific evaluation suites before any training cost is committed.
No generative AI system ships without passing a systematic safety battery. We run hallucination benchmarks, adversarial red-teaming, bias audits, and output consistency tests — then implement factual grounding mechanisms, refusal training, and confidence thresholds to prevent unsafe or inaccurate generations reaching end users.
Deployment is a system engineering challenge, not just model serving. We build vLLM or TGI-based inference stacks with autoscaling, output guardrails, input sanitization, rate limiting, audit logging, and cost monitoring — ensuring the system stays safe, fast, and cost-efficient as usage grows.
Real-World Impact
Enterprise generative AI deployments across industries — each delivering measurable productivity and quality gains from day one.
Core Challenge
Knowledge workers in finance and legal spend 30–40% of their time searching internal documents, policies, and case precedents for answers that exist somewhere in the organization but cannot be surfaced quickly. Generic LLM chatbots hallucinate facts and cite non-existent precedents.
Who Benefits
Law firms, financial institutions, compliance teams, and insurance companies that need accurate, cited answers from their proprietary document corpus — with full audit trails and source attribution for every response.
Request Case StudyCore Challenge
Media companies need to produce high volumes of on-brand written and visual content across formats and languages — at a scale and speed that human teams alone cannot maintain, while preserving editorial quality and brand voice consistency.
Who Benefits
Publishing houses, marketing agencies, e-commerce platforms, and media companies that need a production-grade content pipeline for articles, product descriptions, ad copy, and social media — with human-in-the-loop review workflows.
Request Case StudyCore Challenge
Engineering teams lose significant velocity on code review bottlenecks, repetitive boilerplate generation, and legacy code documentation — tasks where AI assistance can provide 60–80% of the effort while keeping engineers focused on architecture and complex logic.
Who Benefits
Software development teams, platform engineering groups, and tech companies that want a codebase-aware AI assistant integrated into their IDE and CI/CD — fine-tuned on their own repositories to produce contextually appropriate suggestions.
Request Case StudyCore Challenge
Retail customer service teams face high volumes of repetitive queries — order status, product recommendations, returns — that frustrate customers when handled by rigid rule-based bots but are too costly to handle entirely through human agents at scale.
Who Benefits
Retailers, e-commerce platforms, and consumer brands that need a conversational AI layer handling tier-1 customer queries with personalized, product-aware responses — integrated with their CRM, order management, and inventory systems.
Request Case StudyPowered By
Best-in-class models, orchestration frameworks, vector databases, and inference infrastructure — chosen for capability, reliability, and enterprise-grade security.
Frequently Asked
Answers to the questions engineering leaders, CTOs, and product teams ask before starting a GenAI engagement with Presear Softwares.
Ask Our GenAI TeamPartner with Presear Softwares to build generative AI systems that are accurate, safe, and deployed on your terms — not locked in a vendor's cloud.