At Werbooz, we build custom Large Language Models that are trained on your domain, deployed in your infrastructure, and designed to perform under production conditions - not just in a controlled sandbox. We work with CTOs, AI leads, and enterprise architects who've already seen the demo and want to know what it takes to ship. Production rollouts usually combine RAG development for grounded answers and AI integration services to embed models in existing products.
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Most teams reach for a general-purpose model and try to bend it into shape. It works, up to a point. Past that point, you're fighting the model instead of using it. We design LLM architectures from first principles like attention configuration, tokenization strategy, context window sizing, inference pipeline layout. Every decision is made against your specific use case, not against what worked for someone else's. This is model engineering. Not model shopping.
A well-designed prompt is a program. A poorly designed one is expensive noise. We build structured prompt libraries, system instruction frameworks, and chain-of-thought scaffolding that produce consistent, reproducible outputs across your workflows. Not just for one task but for the full range of queries your system will encounter in production. This matters more than most teams realize. A 15% improvement in prompt precision at scale means dramatically fewer failures to handle downstream.
Here's the thing about hallucinations: they're not random. They happen in predictable conditions like when the model is asked about specific facts it wasn't trained on, when the query is too narrow for general training data, when recent information is involved. RAG significantly reduces hallucinations. We architect production-grade RAG pipelines that give your LLM access to live, authoritative data, internal knowledge bases, document stores, databases, CRMs. The model retrieves before it generates. Responses are grounded in real source material, are not just made up from pattern completion.
Your stack already exists. You've spent years building it. We connect your LLM to it without making you rebuild anything. Our engineers integrate custom and third-party LLM APIs into existing ERP systems, CRM platforms, internal tooling, and cloud infrastructure. We handle authentication, rate management, context injection, fallback routing, and error handling. Basically we do the boring work that determines whether an AI feature actually stays live in production.
Pre-trained models know a lot. They just don't know you. We fine-tune all the foundation models like GPT, Claude, LLaMA, Mistral, Falcon, Qwen, and others by using your proprietary datasets. The result - The output isn't a generic model that's been prompted harder. It's a model that has genuinely internalized your domain's language, your output format requirements, and the edge cases that matter to your business. Techniques we use depending on your situation: One pass of fine-tuning rarely gets you there. We build in iteration cycles. That's not a cost item - it's how good fine-tuning actually works.
Hallucinations don't happen because models are careless. They happen because models are trained to produce “plausible” outputs and plausible isn't the same as accurate. Understanding the cause is step one of fixing it. We address hallucinations through a layered approach: No single technique eliminates hallucinations. The combination does.
Most enterprise data isn't text-only. It's PDFs with tables. It's invoices with handwritten notes. It's engineering diagrams next to specification documents. We build multimodal LLM systems that reason across text, images, structured data, audio transcripts, and documents simultaneously. If your business problem involves multiple data types, your AI system probably should too.
Token limits get most of the attention. Effective context is the harder problem. Frontier models support 128K to 1M token windows but attention degrades across long contexts. Information in the middle of a 200K token prompt is reliably underweighted compared to the beginning and end. This isn't a flaw. It's physics of how attention works. We work around it: Your model stays consistent. Even on long, complex sessions.
This is where a lot of enterprise AI projects stall. Legal needs assurances. Compliance needs documentation. Security wants to know what leaves the building. We answer all these up front: Your data doesn't move to a third-party training platform. It doesn't need to.
Shipping a model without benchmarking it is just hope with a deployment pipeline. Most teams test for the obvious stuff like does it answer the question? What they miss is the layer underneath: response latency under concurrent load, relevance degradation on edge-case queries, factual consistency drift across paraphrased inputs, and failure mode distribution at scale. We run structured evaluation frameworks before any model goes near production: A model that passes your benchmarks before deployment gives you something to defend in a production incident. One that didn't - doesn't.
A capable LLM sitting in isolation isn't an AI system. It's an expensive autocomplete. The real work is connecting it. Your model needs to query your database, call your internal APIs, read from your document store, write back to your CRM, and hand off to a human when it hits a decision boundary it shouldn't cross alone. That's orchestration - and it's an engineering problem, not a prompt engineering problem. We design “multi-step LLM workflow architectures” using frameworks like LangChain, LlamaIndex, and custom agent infrastructure: The goal is a system that handles complex, multi-step tasks end-to-end and not one that requires a human to stitch together each step manually.
Inference cost is a real number on a real Profit & Loss Statement. Especially at enterprise scale. We optimize models for production deployment through INT8 and INT4 quantization, quantization-aware training, and knowledge distillation where a smaller model is trained to replicate the behavior of a larger one. The result: lower memory footprint, faster inference, lower cost per query and that too without meaningful accuracy loss on your target tasks.
Proprietary APIs aren't the only path to production-grade AI. Open-source models like LLaMA, DeepSeek, Mistral, Falcon are genuinely capable, and for most enterprise requirements, they're the smarter infrastructure choice. We configure and deploy open-source LLMs for enterprise environments. That means customizing the training pipeline for your data, hardening the infrastructure for your compliance requirements, and getting the model running in your environment and not ours. No per-token billing. No external data dependency. Full control over the weights.
GPT's capabilities are well-established. Getting them to behave correctly inside your specific tools, workflows, and data environment is the part that actually takes engineering work. We embed GPT into internal platforms, CRMs, and business applications with the understanding that makes it production-viable: domain-specific system instructions, access controls, context injection from your internal data sources, and response filtering tuned to your use case. The model performs the way your business needs it to and not the way the demo showed.
Some data can't leave the building. Regulations, IP sensitivity, or contractual obligations make external API routing a non-starter. For these cases, we deploy “fully private LLMs” with complete data isolation, meaning model weights, inference infrastructure, and data pipelines all within your controlled environment. No external endpoints. No shared infrastructure.
Cloud isn't always the answer. Some organizations need model inference to run entirely on local infrastructure that is controlled environments, regulated industries, or setups where latency or data residency requirements rule out cloud deployment. We handle the full on-premises build that is model selection for your compute envelope, deployment configuration, and ongoing tuning. The model runs where your data lives.
Customer feedback is everywhere. Support tickets, call transcripts, reviews, social posts, chat logs and it accumulates faster than any team can manually process it. We build “sentiment analysis pipelines” that can process text, audio, and in some cases image data to surface what customers actually think and not just a sample but the whole volume. The output feeds directly into product decisions, support prioritization, brand monitoring, and campaign refinement. Pattern recognition at the scale that manual review physically can't reach.
Security in LLM systems isn't just about the model. We secure the full chain: Security is an architecture decision made at the start of an engagement. Not a checklist item at the end.
The baseline. You have labeled examples of what good outputs look like; we train the model to replicate that pattern at scale.
Human reviewers rank model outputs; those rankings become training signal. This could be Expensive. Worth it when the output quality needs to be tight.
Updates only a fraction of the model's parameters rather than the full weight set. The result is meaningful behavioral change at a fraction of the compute cost. This is the practical choice for most enterprise fine-tuning engagements.
Takes PEFT further. Only the bias terms in each layer are modified nothing else. Extremely resource-efficient. Best for adapting a model to a new task when compute and time are constrained and the task shift isn't dramatic.
Before any task-specific fine-tuning, the model is pretrained on a large amount of domain-specific text and legal documents, clinical notes, engineering specifications, basically whatever your domain produces. The model internalizes the vocabulary, phrasing patterns, and factual context of your field. Fine-tuning on top of a DAPT model produces consistently better results than fine-tuning on a general base.
A more targeted version of DAPT. Instead of the full domain corpus, the model is warmed up on data closely related to the specific task it will be fine-tuned for. Faster convergence, more focused adaptation.
The model is fine-tuned with quantization constraints active, so it learns to perform well at reduced precision. The result is a model that's already adapted to lower memory footprint before deployment. So rather than one that's quantized after the fact and hopes for the best.
The model learns from examples inside the prompt itself. No parameter updates. No training run. You show it what good looks like, and it generalizes from there - within the same inference call. It's not magic. It has limits. But for tasks where labeled training data is thin or the use case is narrow, in-context learning closes the gap faster than anything else.
Three examples. Five examples. Sometimes ten. That's the input - and for well-scoped tasks, it's enough. We design few-shot prompting frameworks that teach the model to handle niche, domain-specific queries without building a full fine-tuning dataset first. The tradeoff is that performance plateaus earlier than fine-tuning would. The benefit is that you're in production in days, not months.
No examples at all. Just a well-constructed instruction and the model's pretrained knowledge. Zero-shot is where prompt engineering earns its keep. A vague instruction produces vague outputs. A precisely structured prompt with the right framing, constraint specification, and output format can produce results that are genuinely usable without a single labeled example. We build those prompts. The difference in output quality between a mediocre zero-shot prompt and a well-engineered one is larger than most teams expect.
Some problems can't be answered in one step. Financial analysis, legal reasoning, multi-variable decision support and variables like these require the model to work through intermediate steps before arriving at a conclusion. Chain-of-thought prompting makes that explicit. We design prompts that guide the model through the reasoning sequence - not just "answer this," but "here's how to think about this." The output quality improvement on complex reasoning tasks is well-documented and consistent. The model gets things wrong less often when it's been structured to reason first.
Generate the same query multiple times across different reasoning paths. Then pick the answer the model lands on most consistently. It sounds redundant. It works. For mission-critical decisions where a single inference run carries too much variance - medical triage, financial risk assessment, compliance checks - self-consistency decoding reduces hallucination rates meaningfully. You're not trusting one reasoning path. You're sampling the distribution and taking the mode.
Most annotation projects waste effort. They label data uniformly, including examples the model already handles well, and end up spending budget on signal that doesn't move the needle. Active learning inverts this. The model identifies the queries it's least confident about like the ones where its outputs are most uncertain or inconsistent and those become the annotation priority. Human review focuses where it actually matters. Annotation costs drop. Model improvement per labeled example goes up. For organizations with limited labeling budgets and ambitious accuracy targets, active learning is usually the most efficient path from where the model is to where it needs to be.
For engagements with clearly defined scope: a RAG pipeline build, a single-domain fine-tuning project, an API integration. In this model you get a fixed quotation and a predictable timeline. Works well when the problem is well-understood going in.
An embedded AI team of senior LLM engineer, ML researcher, deployment specialist who will be working as a direct extension of your engineering org. Building AI into the core of your product over a sustained period? Then choose this.
If your requirements are genuinely expected to evolve and you are willing to pay for actual resource usage. Scale up or back as the project needs change. Then choose this.
We start by understanding your use case, we don't just blindly follow the feature, but the performance criteria, compliance constraints, latency requirements, and data availability. Most bad AI projects fail here, not later.
Training data quality determines model quality. Our team curates, cleans, and structures your proprietary datasets - removing noise, detecting and stripping PII, enforcing format consistency. No model goes into training on unvalidated data.
We evaluate base model candidates against your specific requirements: reasoning depth, language coverage, context window needs, inference cost, and licensing terms. We recommend based on fit, not familiarity.
We apply the appropriate techniques for your case - SFT, RLHF, LoRA, DAPT, PEFT - and run training on your domain data using distributed compute infrastructure where needed. Iteration cycles are built in from the start.
Every model gets stress-tested before deployment. Accuracy benchmarks. Factual consistency checks. Bias audits. Latency and throughput profiling. Nothing ships until it clears the bar set in Step 1.
Your model goes into production - cloud, on-premises, or hybrid. We integrate with your existing system interfaces, configure monitoring dashboards, and establish alerting on anomalous output patterns.
Language models degrade. Data distributions shift. Queries evolve. We set up automated monitoring pipelines that track output quality, measure embedding drift between incoming queries and training distribution, and trigger review when thresholds are breached. Ongoing retraining cycles are part of enterprise engagements.
$25,000–$60,000
Single-domain fine-tuning, RAG pipeline setup, or API integration into existing tools. Defined scope, limited throughput requirements. Includes dataset prep, base model selection, one fine-tuning pass, cloud deployment, and 30-day post-launch support.
$60,000–$150,000
Multi-domain systems, advanced RAG with hybrid retrieval, custom prompt engineering frameworks, integration into complex enterprise stacks. Includes full data curation pipeline, iterative fine-tuning with RLHF, benchmark evaluation suite, hallucination mitigation architecture, and a 90-day monitoring and iteration window.
$150,000–$350,000+
Full-stack custom LLM: domain-adaptive pretraining, multimodal capability, on-premises deployment, regulatory compliance architecture, high-volume production inference.
A Large Language Model is a transformer-based neural network trained on text at scale. The training process teaches the model to predict language - and through that, to reason, summarize, translate, and generate. Off-the-shelf models are trained on general data. They're capable of a lot. They're optimized for nothing in particular. Custom LLM development changes that. It takes a foundation model and shapes it - through fine-tuning, alignment, and retrieval architecture - into a system that performs reliably on your specific business tasks. Not as a party trick. As a production system. The work spans data engineering, model training, alignment techniques, inference optimization, and deployment infrastructure. It's not prompt engineering with extra steps. It's AI system development.
Internal document search that understands intent, not just keywords. Engineering wikis, compliance databases, product catalogs - users ask questions and get answers rather than a list of documents to read through.
LLMs fine-tuned on your codebase generate context-aware code suggestions, surface security vulnerabilities during PR review, and enforce your team's conventions - without requiring manual rule configuration.
Extract structured data from unstructured legal, financial, and compliance documents. Flag risk clauses, identify anomalies, generate summaries. At the volume that manual review can't touch.
What clients say
“Werbooz engineered our freight marketplace with remarkable precision and ownership. Their ability to execute complex systems fast gave us confidence to compete globally while maintaining performance, reliability, and seamless user experience.”
Nnamdi George Okafor
CEO, Kargoplex
“Werbooz delivered exceptional work across both Fawwnity and Anahama, truly understanding our vision. Their consistency and quality made us repeat clients, and we confidently recommend Werbooz to anyone building seriously.”
Priya Sharma
Founder, Anahama | Co-founder, Fawwnity
“Working with Werbooz was a great experience. They understood our requirements clearly and delivered everything with care and precision. The platform feels smooth, thoughtful, and exactly aligned with our expectations.”
Subodha Kumar
Executive Editor, MBR Journal
“Working with Werbooz was a smooth and enjoyable experience. They understood our vision clearly and delivered exactly what we needed with great attention to detail and thoughtful execution throughout.”
Kalyan Singhal
Publisher & Co-Editor in Chief, MBR Journal
“Werbooz built our entire AI-powered infrastructure with exceptional clarity and execution. From co-pilot systems to user flows, everything works seamlessly, enabling meaningful career conversations at scale without complexity.”
Tejas N Gowda
CEO, Develup
“Werbooz consistently delivers high-performance execution across our platforms. Their ability to handle complex systems and maintain speed, stability, and precision makes them a reliable partner for our growing infrastructure.”
Farhan Ahmed
Associate Director, TransFi
“Werbooz built our platform and automation systems with a strong focus on efficiency and scalability. Everything runs smoothly, from website to notifications, enabling us to manage operations without friction.”
Adit Chouhan
Founder, Weekendo
“Werbooz delivered a unique platform combining e-commerce with storytelling effortlessly. Their execution and technical expertise created an engaging, smooth experience that stands out while supporting our growing user base.”
Aanya Jai
Founder, Probehave
They solve different problems. Fine-tuning shapes how the model behaves - its tone, reasoning style, output format, and domain vocabulary. RAG controls what the model knows at query time by grounding responses in retrieved source material. If you need the model to stay current with changing information or cite specific documents, RAG is the right primary tool. If you need behavioral alignment to your domain, fine-tuning is necessary. Most mature enterprise deployments need both - fine-tuning for behavioral precision, RAG for factual accuracy. Treating them as alternatives rather than complements is one of the more common scoping mistakes we see.
Your data stays in your environment. We establish isolated training infrastructure within your VPC or on-premises setup - your data doesn't transit to external training platforms. Before any training begins, we run automated PII detection and stripping. Access controls restrict who on the engineering team can touch training data at all. Audit logs cover the full data lifecycle from ingestion through model export. Compliance documentation - SOC 2, HIPAA, GDPR - is produced as a formal deliverable, not an afterthought.
The advertised token count is only part of the story. Attention in transformer models degrades over long contexts - specifically, information positioned in the middle of a very long prompt is consistently underweighted compared to content near the beginning or end. This is well-documented and affects every current architecture. We address it practically: retrieval systems that inject only the highest-relevance content into context, dynamic summarization to compress prior conversation state without semantic loss, and sliding window approaches for sequential document processing. The goal is making the model reliably attend to the right content - which is a harder problem than simply fitting more tokens.
Open-source models (LLaMA 3, Mistral, Falcon, Qwen) require real upfront engineering investment. Infrastructure, training runs, deployment, maintenance - these aren't trivial. But once deployed, inference cost per query drops dramatically versus per-token API pricing, your model weights stay entirely in your control, and no enterprise data ever goes to an external endpoint. Proprietary APIs (GPT-4, Claude, Gemini) have lower initial engineering overhead but carry ongoing per-token costs that compound significantly at volume, plus dependency on vendor API availability, pricing changes, and data transmission to external endpoints. For regulated industries, high-volume production systems, or organizations with strong IP requirements, open-source models typically produce better total cost of ownership once the scale math is run. We model this explicitly during scoping so you go in with eyes open.
Model drift happens when real-world query distribution diverges from training data - and it happens to every production model eventually. We instrument deployments with monitoring pipelines that track output confidence distribution, flag anomalous response patterns, and measure embedding distance between live queries and the training distribution baseline. When drift metrics breach configured thresholds, automated alerts trigger review. Enterprise engagements include scheduled retraining cycles built into the service agreement, so the model stays aligned with current data and evolving business requirements rather than quietly degrading.
Related capabilities
Werbooz covers product, engineering, AI, cloud, and growth under one roof. If your roadmap touches adjacent work, these are sensible places to start.
Let's talk
One short brief. One business day. A senior engineer's honest read on scope, timeline, and cost-whether or not you end up working with us.
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