Generative AI & LLM Development
Enterprise-Grade
Generative AI.
Not Just API Wrappers.
Anyone can call an API. We build production systems with guardrails, context management, cost optimization, and enterprise security — that actually work at scale.
Generative AI solutions
Six production GenAI systems we ship.
Not prototypes. Not demos. Production systems running at enterprise scale — with the security, latency, and reliability your business actually requires.
Purpose-built AI applications for your specific workflows — not off-the-shelf tools stretched to fit. From internal knowledge assistants to customer-facing AI features inside your product.
Query your documents, databases, and knowledge bases with natural language — and get grounded, accurate answers with citations. The foundation of most enterprise GenAI deployments.
In-app assistants embedded inside your product or internal tools — surfacing insights, drafting content, automating tasks, and helping users accomplish more without leaving their workflow.
Marketing copy, technical documentation, product descriptions, and reports — generated at scale with brand voice controls, quality guardrails, and human review workflows built in.
Extract, summarize, compare, and analyze unstructured documents — contracts, reports, medical records, financial filings — with accuracy and auditability that manual review can’t match at scale.
Multi-turn, context-aware dialogue systems that maintain conversation history, understand nuance, take actions in connected systems, and know when to escalate to a human.
Why AddWeb Is Different
Why we’re not just another AI shop.
The gap between a GenAI prototype and a production system that enterprises trust is vast. These are the five engineering disciplines that bridge it, and most AI agencies skip them.
01
Production-First Engineering
We architect for the 1,000th request, not the first demo. Load testing, failure modes, graceful degradation, latency budgets, and horizontal scaling are built into the design — not bolted on after launch day.
Uptime: 99.9% · p95 <2s · auto-scaling
02
Security by Design
PII detection and redaction, data isolation between tenants, prompt injection defense, API key management, full audit logging, and zero-retention API configurations for sensitive data. Security isn’t a checklist — it’s architecture.
SOC 2 · Azure OpenAI · self-hosted options
03
Token Cost Optimization
LLM inference costs compound fast at scale. We implement semantic caching, prompt compression, model routing (use GPT-4 only when needed), and response streaming — typically reducing inference costs by 40–70% vs naive implementations.
Avg 52% cost reduction vs baseline
04
Hallucination Control
RAG grounding with source citation, confidence scoring that flags low-confidence outputs, constrained generation for structured responses, and human-in-the-loop verification for outputs that drive real decisions.
Confidence threshold · citation required
05
Human Oversight Loops
Every production GenAI system needs a path back to human judgment. We build escalation triggers, feedback collection, correction workflows, and continuous improvement pipelines, so the system gets better with every interaction, not worse.
RLHF-ready · correction logging
06
The result
Production GenAI systems that your legal, security, and finance teams can actually approve, because the engineering decisions were made for enterprise, not demo day.
30+ enterprise deployments · 0 security incidents
GenAI Across Industries
GenAI applications across high-value industries.
The same LLM technology produces very different outcomes depending on domain expertise in the build. Here’s what GenAI looks like in your industry, built by people who understand it.
eCommerce
Personalize every product page, answer shoppers in natural language, and generate product content at scale, without a content team that can’t keep pace with your catalog.
Legal
Contract review, due diligence, and legal research that would take a paralegal team days, completed in minutes, with full citations and human attorney review workflows built in.
Healthcare
Clinical documentation, patient communication, and research synthesis are built to HIPAA standards with the audit trails and data isolation that healthcare demands.
Finance
Earnings report generation, compliance Q&A, and customer service at scale, with the hallucination controls and audit trails that regulated financial environments require.
How We Build GenAI
Five phases from idea to production, GenAI.
One chatbot brain. Deployed across every channel, so your users get consistent, intelligent support whether they’re on your website, WhatsApp, or Slack.
Discovery
Use Case Validation
Validate the use case, assess data quality, select models, define accuracy targets and latency budgets before writing a line of code.
Prototype
POC on Real Data
Build a working prototype with your actual data and evaluate accuracy, latency, and cost on realistic inputs, not curated examples.
Engineering
Production Architecture
Build the production system, security, scalability, cost optimization, monitoring, and all the enterprise requirements that prototypes ignore.
Deployment
MLOps & Monitoring
Deploy with CI/CD pipelines, real-time monitoring of accuracy and latency, drift detection, and feedback collection from day one.
Optimization
Fine-Tune & Reduce Costs
Use production data to fine-tune models, reduce token consumption, optimize caching, and continuously improve accuracy from real-world feedback.
Engagement Options
Four engagement models. One outcome, GenAI that ships.
Structured to match where you are, a defined project, exploratory work, or a dedicated team embedded in your organization.
AI MVP / Proof of Concept
$5K – $25K
Ideal for testing use cases, internal tools, or first AI rollout with minimal risk.
Fixed-Scope Project
$25K – $120K
Defined deliverables · Fixed price · 6–16 weeks
Dedicated GenAI Team
Retainer
Omnichannel · Enterprise security · Dedicated support
Let’s build your GenAI solution.
30 minutes. No sales pitch. Just a technical discussion about your use case, your data, and what’s genuinely possible, with honest timelines and costs.
Case Studies
AI Success Stories
We let the numbers do the talking. Here’s what happens when AI is built right and shipped into production.
SEO Stream
We created an intelligent automation solution named SEO Stream that changes the way SEO teams perform backlink analysis and domain research. With AI-enabled quality verification, automated workflow coordination with N8N, and a powerful reporting system, SEO Stream allows the removal of repetitive manual work and gives a chance to make decisions based on data and…
City Outreach
We’ve built a digital platform for City Outreach that will make their website more appealing, easier to use, and better able to support people in need.
FAQs
The technical and commercial questions every GenAI buyer asks.
Answered directly, including the nuanced ones about model selection, hallucination, data privacy, and cost.
It depends on the use case. GPT-4o excels at reasoning, code, and tool use. Claude 3.5 Sonnet outperforms on long documents, nuanced analysis, and instruction following. Open source (Llama 3.1, Mistral) offers cost savings and full data privacy for sensitive deployments. We can build multi-model architectures that route to the right model per query — and we’ll recommend the optimal setup after understanding your specific requirements.
Multiple overlapping techniques: RAG grounding ensures responses are tied to retrieved source documents, confidence scoring flags low-certainty outputs for human review, citation requirements force the model to reference specific content rather than synthesize, constrained generation for structured outputs, and human-in-the-loop checkpoints for critical decisions. No single technique is sufficient, we combine all of them.
Yes — we architect for data privacy from the start. Options include Azure OpenAI Service (your data never leaves your Azure tenant), self-hosted open source models (no external API calls at all), and standard API configurations with training-data opt-out enabled. We implement PII redaction before any data reaches the LLM, and can deploy the entire stack in your private cloud for maximum control.
Simple single-purpose chatbots start around $25K. RAG applications with enterprise integrations typically range $50K–$100K. Complex multi-model systems with fine-tuning, custom infrastructure, and enterprise security can exceed $150K. We provide detailed fixed-price estimates after a free 30-minute technical discovery call where we understand your specific requirements.
Simple chatbots: 4–6 weeks. RAG applications with document ingestion and enterprise integration: 8–12 weeks. Complex multi-model systems with custom training and MLOps: 12–20 weeks. We always deliver an MVP first and iterate — so you’re seeing working software within weeks, not months. The final timeline depends heavily on integration complexity and data readiness.
Yes. We offer fine-tuning for OpenAI models (GPT-3.5, GPT-4), full supervised fine-tuning for open source models (Llama, Mistral), and RLHF-based alignment for complex instruction following. We also recommend RAG as an alternative — often more cost-effective and easier to update than fine-tuned models. We’ll recommend the right approach based on your data volume, use case, and update frequency requirements.
