Every company that uses off-the-shelf AI gets the same answers from the same models trained on the same public data. That's fine for writing emails. It's useless for the work that actually matters: answering questions only your data can answer, generating documents only your experts could write, making decisions only your business context can inform. We build custom generative AI trained on your data, your domain, and your rules.
Custom LLMs. RAG pipelines. Fine-tuned models. Production-grade.
Build Your Custom AI"Just use ChatGPT" is the 2024 equivalent of "just Google it." Every foundation model has different strengths, different cost profiles, and different failure modes. Choosing wrong means rebuilding from scratch three months later. We've shipped production systems on all of them. We know which one fits your problem.
Strongest general reasoning across the widest range of tasks. We deploy GPT when projects need multi-step logic chains, code generation, and broad world knowledge. Its ecosystem is the deepest: function calling, vision, audio, and a library of plug-in integrations that accelerate time-to-production.
We deploy Claude on agentic coding workflows, complex refactors, and multi-file architecture tasks where other models lose the thread. It follows nuanced instructions precisely and produces carefully structured output. Its massive context window also makes it our go-to for document pipelines where entire contracts and compliance manuals fit in a single pass.
When data sovereignty is non-negotiable, Llama runs on your infrastructure with zero external API calls. No vendor lock-in. No per-token fees at scale. We fine-tune Llama for clients in healthcare, defense, and finance where sending data to a third-party API is not an option.
Gemini plugs directly into the Google Cloud ecosystem, making it the natural choice for teams already running on GCP. Strong multimodal capabilities, massive context windows, and enterprise-grade infrastructure out of the box. And when the project calls for Mistral, Grok, Cohere, or the next model that hasn't launched yet, we deploy that too.
The difference between a chatbot that hallucinates and an AI that performs like an expert comes down to one question: how do you connect it to your data? There are three approaches, and only one is right for your problem.
A legal team needs to search 10 years of case law and get cited answers in seconds. They don't need a new model. They need their existing model connected to their documents.
Your data becomes the AI's reference library. Every answer is grounded in your actual documents, databases, and knowledge bases. The model doesn't guess. It looks up the answer, generates a response, and shows you exactly where it found the information.
A medical device company needs AI that writes FDA submission documents in exactly the right format, with exactly the right terminology. Generic AI gets the tone wrong every time.
Take a foundation model and teach it your domain, your language, your standards. The model internalizes your expertise. It doesn't just reference your data. It thinks like your best specialist. Outputs that used to require senior review come out right the first time.
A pharmaceutical company needs neural networks that analyze three-dimensional molecular structures. No existing model understands this data type. The architecture needs to be designed from the ground up.
When your problem doesn't fit any existing model, we build one. Custom architecture designed for your specific data types, your specific outputs, your specific accuracy requirements. You own every weight, every parameter. No dependency on any provider.
Clinical notes consume 2 hours of every physician's day. Discharge summaries pile up. Research teams drown in published literature they can't read fast enough. We build AI that generates clinical documentation from visit notes, synthesizes research across thousands of papers, and flags drug interactions before they reach a patient.
2+ hours of documentation time saved per physician per day
A junior associate reviews one contract per hour. AI reviews one per minute and flags every non-standard clause, missing provision, and regulatory risk. We build systems that draft contracts from precedent, research case law with citations, and monitor regulatory changes across jurisdictions in real time.
60x faster contract review with clause-level precision
Quarterly earnings analysis. Regulatory filings. Risk narratives. Fraud investigation summaries. All follow patterns. All consume analyst hours that could go toward actual analysis. We build AI that generates these reports from structured data, cross-references against compliance requirements, and flags anomalies worth human attention.
Analyst time redirected from writing to thinking
You have a catalog of 10,000 SKUs and product descriptions written by 15 different people over 8 years. Half are outdated. None are SEO-optimized. We build AI trained on your brand guidelines that rewrites your entire catalog, keeps it current as products change, and generates new descriptions the day a product launches.
Entire catalog rewritten and optimized in days, not months
Your team spends 40 hours on a proposal that's 70% boilerplate from the last five proposals. The custom 30% is where the deal is won or lost. We build AI that generates the 70% from your proposal library and win/loss data, so your team spends all 40 hours on the 30% that matters.
70% of proposal boilerplate automated
A professor creates one version of course materials for 200 students with 200 different knowledge gaps. A researcher needs to read 500 papers before writing a literature review. We build AI that adapts learning content to individual comprehension levels and synthesizes research at the speed it's published.
Personalized content at the scale of a lecture hall
A law firm deployed a generic AI assistant for case research. It cited three court cases that didn't exist. The opposing counsel found out in discovery. That's not a technology problem. That's a system design problem.
Hallucination is the number one fear with generative AI, and the fear is justified. In customer support, a hallucination is embarrassing. In a legal document, it's a malpractice claim. In a medical context, it's dangerous. We don't hope the model gets it right. We engineer systems where getting it wrong is structurally difficult.
Every generated answer includes citations to the specific documents, paragraphs, and data points it used. Your team can verify any claim in seconds. If the AI can't find the answer in your data, it says so instead of guessing.
For factual questions, we constrain the model to only use information it retrieved from your documents. No creative extrapolation. No filling gaps with training data. Retrieved facts only, or an honest 'I don't know.'
Every response carries a confidence score. Low-confidence answers get flagged for human review automatically. You set the threshold. A medical application might require 95% confidence. An internal FAQ might accept 80%. You decide where the line is.
A second AI pass validates claims against source material before delivering the response. Contradictions get caught. Unsupported statements get filtered. The user never sees an answer that hasn't been cross-checked.
Every system we deploy includes a human-in-the-loop review pathway. AI proposes. Your team disposes. The model gets smarter from every correction.
The phrase "custom LLM" sounds like it costs millions. For 95% of businesses, it doesn't. You're not training GPT from scratch. You're taking a powerful foundation model and making it an expert in your domain. Here are the four levels of customization, from lightest to deepest.
Careful system prompts and few-shot examples that shape a foundation model's behavior. No training required. Deploy in days. Best for straightforward use cases where the model already has the knowledge and just needs direction.
Your proprietary data connected to a foundation model through a retrieval pipeline. The model accesses your knowledge at query time. No model weights change. You update the knowledge base whenever your data changes. Most businesses start here.
Train an existing model on thousands of your domain-specific examples. The model's weights change permanently. It internalizes your patterns, your terminology, your quality standards. Produces expert-level output without retrieval overhead.
Purpose-built architecture trained from scratch on your data. Maximum control. Maximum performance for your specific task. You own every weight, every parameter. Zero dependency on any provider's model or pricing changes.
What data do you have? Where does it live? How clean is it? What's missing? We audit your data landscape, define the problem specification, and design the architecture. Which approach fits: RAG, fine-tuning, or custom? Which model? Which infrastructure? Every decision is backed by your actual data, not assumptions.
We build the data pipeline: ingestion, cleaning, chunking, embedding, indexing. For fine-tuning projects, we curate and structure training data from your best examples. The foundation is everything. A RAG pipeline with bad chunking gives bad answers. We get the plumbing right before we turn the water on.
We build the system, then test it against your actual use cases. Not synthetic benchmarks. Real questions your team asks, real documents your business produces, real edge cases that trip up generic AI. We iterate until the output meets your standard, not ours.
Monitoring, logging, error handling, rate limiting, cost optimization, security review. The gap between a demo that impresses and a system that survives production traffic. We handle the engineering so your team focuses on using the AI, not babysitting it.
AI systems drift. Data changes. New models release. We track accuracy, latency, cost, and user satisfaction continuously. When performance degrades, we retrain. When better models ship, we evaluate whether upgrading improves your specific metrics. The model you launch today is the worst version you'll ever have.
Your data is your competitive moat. We build the AI that makes it unassailable.
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