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ChatGPT Knows Everything About the Internet and Nothing About Your Business.

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

The Wrong Model Wastes Months.
We've Tested Every One So You Don't Have To.

"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.

GPT
OpenAI
The workhorse

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.

Best for: Complex reasoning chains, multi-modal tasks, rapid prototyping
CLAUDE
Anthropic
The coder

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.

Best for: Agentic coding, complex refactors, long document analysis, safety-critical applications
LLAMA
Meta (Open Source)
The one you own

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.

Best for: On-premise deployment, data sovereignty, cost-sensitive scaling
GEMINI + MORE
Google & Others
The full bench

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.

Best for: GCP-native teams, multimodal workloads, vendor flexibility

Three Ways to Make AI Yours

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.

RAG

Retrieval-Augmented Generation

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.

Best for:Knowledge bases, document Q&A, customer support, internal search
Timeline:2 to 4 weeks
How it works:We build a retrieval pipeline that chunks your data, creates embeddings, and feeds relevant context to the model at query time. The model generates answers from your data, not from memory.
FINE-TUNING

Domain-Adapted Models

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.

Best for:Specialized content generation, domain-specific analysis, brand voice, classification
Timeline:3 to 6 weeks
How it works:We curate training data from your best examples, structure it for model learning, and run supervised fine-tuning. The result is a model that produces domain-expert output without retrieval overhead.
CUSTOM TRAINING

Purpose-Built Architecture

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.

Best for:Proprietary data types, unique processing needs, competitive differentiation
Timeline:8 to 16 weeks
How it works:We design the architecture, curate and prepare training data, train the model on optimized infrastructure, and iterate until performance exceeds your requirements. You own the model entirely.
RAG
Fine-Tuning
Custom
Your Data Volume
Any size
1,000+ examples
10,000+ examples
Time to Deploy
2 to 4 weeks
3 to 6 weeks
8 to 16 weeks
Best When
Answers must cite sources
Output must match your style
No existing model fits your data
Data Privacy
Data stays in your pipeline
Used during training only
Fully self-contained

What Generative AI Actually Does for Your Business

KNOWLEDGE SYSTEMS

Your Entire Organization, Searchable in Seconds

Before: A new hire spends 3 months figuring out where answers live across Slack, Notion, Google Drive, and tribal knowledge that only long-timers carry.
After: They ask one question and get a cited answer in seconds. Institutional knowledge stops walking out the door when people leave.
3 months of ramp-up compressed to days
DOCUMENT GENERATION

First Drafts That Only Need a Final Read

Before: Your analysts spend 80% of their time writing first drafts of contracts, proposals, and compliance filings. The remaining 20% is the actual thinking.
After: AI generates drafts from your templates, your data, your formatting rules, your compliance requirements. Your team reviews and refines. They get back the 80%.
80% reduction in draft creation time
CONTENT PIPELINES

Your Brand Voice at 100x the Speed

Before: One writer produces 3 blog posts per week. Product descriptions sit in a backlog. Email campaigns launch weeks late because copy is the bottleneck.
After: Content factually grounded in your product data and brand guidelines. Not generic AI output. Content that sounds like your best writer on their best day, produced in minutes.
100x content throughput with consistent voice
CODE ASSISTANTS

Your Codebase, Your Standards, Built In

Before: New engineers take 6 weeks to ship their first production PR. Senior engineers answer the same architecture questions every sprint.
After: Internal tools trained on your codebase, your patterns, your style guide. New engineers ship production-quality code in their first week. Seniors stop being human search engines.
New engineer productivity in week 1, not month 2
DATA SYNTHESIS

When Real Data Isn't Enough

Before: You have 100 real examples of a rare event. Every ML vendor says you need 10,000. The project dies before it starts.
After: Synthetic data generation bridges the gap. Privacy-safe, statistically valid, purpose-built for your model's training needs. 100 real examples become 10,000 useful ones.
100x dataset expansion for rare-event training
INTELLIGENT ANALYSIS

Feed It Data. Get Back Decisions.

Before: An analyst spends 3 days reading 200 pages of competitor filings to produce a 2-page summary. By the time it's done, the information is already aging.
After: The AI reads everything, identifies patterns across documents, and delivers structured insights with citations. Not dashboards that require interpretation. Conclusions that require a decision.
3-day analysis cycle reduced to minutes

Where Generative AI Changes Everything

HEALTHCARE & PHARMA

Documentation That Writes Itself, Research That Reads Itself

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

LEGAL

Every Contract Reviewed. Every Clause Caught.

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

FINANCIAL SERVICES

The Report Nobody Has to Write Anymore

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

E-COMMERCE & RETAIL

10,000 Product Descriptions. One Brand Voice.

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

PROFESSIONAL SERVICES

The Proposal That Writes Its Own First Draft

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

EDUCATION & RESEARCH

Curriculum That Adapts. Research That Summarizes Itself.

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

We Build AI That Doesn't Make Things Up

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.

Source Attribution

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.

Retrieval-Only Answers

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.'

Confidence Scoring

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.

Validation Chains

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.

What "Custom LLM" Actually Means

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.

LEVEL 1
Timeline: DaysInvestment: Lowest

Prompt Engineering

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.

LEVEL 2
Timeline: 2 to 4 weeksInvestment: Moderate

RAG + Prompt Tuning

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.

LEVEL 3
Timeline: 3 to 6 weeksInvestment: Higher

Fine-Tuned Foundation Model

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.

LEVEL 4
Timeline: 8 to 16 weeksInvestment: Highest

Custom-Trained Model

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.

From Raw Data to Production AI

01
WEEK 1

Data Audit and Strategy

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.

Deliverable: Data audit report, architecture blueprint, model selection rationale
02
WEEKS 2 to 3

Data Pipeline and Model Setup

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.

Deliverable: Production data pipeline, curated training dataset, development environment
03
WEEKS 3 to 5

Build and Evaluate

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.

Deliverable: Working prototype with evaluation metrics on your test cases
04
WEEKS 5 to 6

Production Hardening

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.

Deliverable: Production-ready deployment with monitoring dashboard
05
ONGOING

Monitor, Measure, Improve

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.

Deliverable: Monthly performance reports, quarterly model evaluations

This is for Founders Who...

Your data is your competitive moat. We build the AI that makes it unassailable.

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