LLM features that ship, get used, and do not blow up your risk profile
You do not need another AI prototype. You need stable, auditable features inside your web app that your security team can sign off on, your CFO can budget for, and your users actually adopt.
BYBOWU is a Phoenix-based web and app development team working with companies across the US and worldwide. We design and implement custom large language model (LLM) integrations for enterprise web applications, focused on concrete use cases like RAG search, Q&A, summarization, redaction, and tool calling, wired into your existing stack with guardrails, monitoring, and KPIs.
If you are a founder, product owner, or operations lead, we help you decide where AI belongs in your roadmap, then build it in a way that is safe, observable, and maintainable by your internal team.
The problems we usually walk into
Most teams are already experimenting with AI. The trouble is turning experiments into reliable production features. Common patterns:
- Support and operations are underwater. Agents, CSMs, and ops teams dig through tickets, contracts, and KB articles to answer the same questions over and over.
- Onboarding and activation lag. New customers struggle to configure complex products, so adoption stalls and support volume spikes.
- Knowledge is scattered. Information lives in PDFs, internal wikis, shared drives, and legacy tools. Search is slow, incomplete, and not trusted.
- Security and compliance are nervous. AI pilots sit outside normal controls, with unclear logging, data handling, and change management.
- Costs are opaque. Token usage, retries, and unbounded prompts make it hard to predict spend or guarantee latency.
Our LLM integration work is built to address these directly: faster answers, fewer escalations, less swivel-chair work, and clear ownership of risk and costs.
How we design and implement your LLM stack
This service is a focused part of our broader AI Solutions & Custom AI Development offering. We are vendor-neutral and opinionated about architecture, not tied to a single provider.
1. Discovery and use-case definition
- Clarify business goals: what has to move, such as resolution time, activation rate, review time, CSAT, or cost per ticket.
- Map data sources: knowledge bases, PDFs, tickets, CRM/ERP, logs, and internal tools that actually power the workflow.
- Capture constraints: security, compliance, data residency, existing SLAs, and internal review cycles.
- Identify true pilot scope: one or two high-impact journeys where AI can help without rewriting your entire product.
2. Architecture and model strategy
Once we know the job to be done, we design a stack that is realistic for your team to operate.
- Gateway and orchestration. A secure API layer that handles routing, retries, timeouts, caching, and per-tenant cost controls.
- Retrieval-augmented generation (RAG). Document ingestion, chunking, and embeddings, backed by a vector store such as pgvector, Pinecone, or Weaviate for grounded answers with citations.
- Tool / function calling. Controlled interfaces into your CRM, billing, ticketing, or internal services so the model can take safe actions, not just generate text.
- Security and safety. PII scrubbing, content filters, allow/deny lists, and policy checks that line up with your compliance obligations.
- Observability. Structured traces for prompts, latencies, token usage, model versions, and quality metrics that your team can actually inspect.
3. Implementation and integration into your app
We integrate LLM capabilities directly into your existing web application so they feel like part of the product, not a bolted-on chatbot.
- Back-end integration. Stable, versioned APIs with strongly-typed schemas, predictable error handling, and clear SLAs.
- Front-end UX. Assistants, smart search, inline suggestions, and summary panels with streaming responses and clear source citations.
- Security and DevOps. CI/CD hooks, environment separation, key rotation, and monitoring tied into your existing observability stack.
- Alignment with your tech stack. We are comfortable fitting into modern stacks powered by tools like Laravel, Next.js, React, or Django.
4. Evaluation, guardrails, and hardening
- Define golden test sets and human review loops to benchmark accuracy, coverage, and tone.
- Automated regression tests when prompts, models, or RAG settings change.
- Safety scaffolding for jailbreak attempts, prompt injection, and policy violations.
- Performance and cost tuning with streaming, caching, hybrid search, and model tiering strategies.
5. Rollout, training, and handover
- Phased rollouts with pilots, limited betas, and clear expansion criteria tied to specific KPIs.
- Documentation your team can live with: prompt specs, pipeline diagrams, API contracts, and runbooks.
- Training sessions for product, support, and engineering so you can own and extend the platform without us in the loop for every change.
What you can order
- LLM Support Assistant Pilot — A production-ready in-app assistant for your support or success team, including RAG over a defined corpus, guardrails, and basic analytics. Ideal if you want one high-impact use case in 6–8 weeks.
- Domain Q&A and Knowledge Search — End-to-end RAG implementation over your docs, policies, and internal KB, with semantic search, citations, and admin tools for content owners.
- Summarization and Redaction Service — Secure APIs that turn long-form content (tickets, transcripts, reports) into policy-safe summaries with PII redaction, ready to plug into your existing workflows.
- Tool-Calling and Workflow Automation — Design and build of controlled tool interfaces so your LLM features can safely create tickets, update accounts, and trigger tasks with full auditability.
- LLM Platform Foundation — A reusable gateway, monitoring, and evaluation stack that standardizes how your org talks to multiple LLM providers and models.
- LLM Architecture and Risk Review — A short, focused engagement where we review an existing AI initiative, surface risks, cost leaks, and opportunities, and give you a concrete improvement plan.
What you actually get: deliverables and outcomes
We measure success by shipped capabilities and business impact, not slide decks.
- LLM gateway and orchestration API. A secure, documented API layer that routes requests to one or more models, with quotas, logging, and clear SLAs.
- RAG search over your private data. Ingestion pipelines, embeddings, vector indexes, and retrieval logic that return grounded context and citations.
- In-product assistants and flows. Embedded chat, guided setup, smart forms, and "explain this" or "summarize this" features inside your existing UI.
- Summarization and redaction endpoints. Reusable services for operations, legal, or finance to triage, summarize, and anonymize long content.
- Tool-calling integrations. Controlled function interfaces into your app (create ticket, update record, schedule task) with traceable, auditable execution.
- Monitoring and dashboards. Views of token spend per feature, latency by model, answer quality, and safety violations that leadership can actually understand.
Common business results include:
- Lower support costs from higher self-service rates and faster time to resolution.
- Higher activation and product adoption thanks to contextual in-app guidance.
- Shorter review cycles for policies, memos, and reports with consistent redaction and compliance checks.
- More predictable AI spend with clear levers for trading off cost, latency, and quality.
Models, hosting, and enterprise controls
We help you choose a model and deployment approach that aligns with your risk, budget, and performance requirements.
Vendor-neutral model selection
We work with major commercial and open-source models, including:
- OpenAI models such as GPT-4.1 and GPT-4o
- Anthropic Claude 3.x family
- Google Gemini
- Cohere Command series
- Open-source options including Meta Llama and Mistral families
We do not chase leaderboard scores. We benchmark a shortlist against your evaluation set for accuracy, latency, safety behavior, and cost per task, then recommend a primary model and reasonable fallback strategy.
Hosting and deployment options
- Managed SaaS APIs for speed to market and best-in-class capabilities.
- VPC-isolated endpoints with private networking if your security team requires stricter data governance.
- Self-hosted open models when you need maximum control over data, latency, or cost.
Security, privacy, and compliance
- Least-privilege access, network isolation, and KMS-managed secrets as a baseline.
- Explicit control over training rights so providers do not train on your data unless you approve it.
- Signed URLs and encrypted storage for sources, with row and column level permissions on retrieval.
- Audit trails capturing prompts, retrieved documents, tool calls, and output decisions.
- Support for working within common frameworks such as SOC 2, HIPAA, or GDPR, in collaboration with your security team.
Proof it works in the real world
Marketplace support search
For a fast-growing marketplace (similar in complexity to projects like BEZET and SixZeros), we designed AI-assisted support search so agents could quickly find accurate policy and product information, reducing manual digging across multiple tools.
SaaS onboarding assistant
On a B2B SaaS-style platform, we implemented an in-app assistant that helps new users understand features, configure settings, and trigger key account actions, improving activation without redesigning the entire product.
Summarization and redaction for operations
For a team handling high volumes of operational documents, we deployed a summarization and redaction service in a private environment, so staff could process more cases with consistent policy enforcement and no extra risk to sensitive data.
To see how we think about product and engineering more broadly, you can browse our portfolio and blog, then we can map the closest pattern to your stack and constraints.
Why choose BYBOWU for LLM integration
- Product thinking first — We start with user journeys and business metrics, not with a model name, so what we build actually gets used.
- Engineering depth — Our team builds full web and mobile products every day, from web apps to mobile apps, so we know how to integrate AI into real-world systems and constraints.
- Pragmatic risk management — Guardrails, observability, and cost controls are part of the first release, not a phase-two afterthought.
- Direct access to senior people — You work with experienced engineers and product leads, not a rotating cast of juniors.
- Global perspective, local accountability — Headquartered in Phoenix, AZ, we work comfortably with US and international teams across time zones and compliance environments.
Questions founders usually ask
What kind of budget do we need for an initial LLM project?
Most serious pilots land in the same range as a focused web feature: enough to cover discovery, architecture, implementation, and initial hardening. After an initial call and a quick discovery, we will propose either a fixed-scope pilot or a clear monthly retainer, not an open-ended experiment.
How long until we see something in production?
If your data is accessible and your stack is reasonably modern, we can typically ship a narrowly scoped pilot such as a support assistant, Q&A, or summarization service in 6–8 weeks, then iterate based on real usage.
Do we have to pick a single LLM provider now?
No. We usually design an abstraction layer that lets you change models or add providers later with minimal product changes. During the pilot we will recommend a primary model and sensible fallback options based on your evaluation set.
Can you work with our internal security and compliance teams?
Yes. We expect security and compliance to be at the table for enterprise work. We document data flows, hosting options, and controls in plain language and adjust the architecture to meet your internal requirements.
What happens after the initial launch?
You can keep our team involved through our support and maintenance services, or fully take over with your own engineers. We provide documentation, training, and, if needed, a roadmap for future features so the work does not stall after version one.
We already have an AI pilot. Can you review or improve it?
Yes. We often start with an architecture and risk review of an existing implementation to surface reliability issues, security gaps, and cost problems, then either hand you a plan or help your team execute it.
How engagement works
Busy teams do not need a six-month strategy exercise before they see value. We keep the process focused.
1. Initial call (30–45 minutes)
- Clarify use cases, constraints, and success metrics.
- Review your existing product, data, and architecture at a high level.
- Identify quick-win pilots versus deeper platform work.
2. Short discovery and proposal
- Outline architecture options, model choices, and integration points.
- Define a pilot scope with concrete timelines, responsibilities, and KPIs.
- Agree on pricing and engagement format that fits how your team works.
3. Pilot, then scale
- Pilot (roughly weeks 1–6). Build a tightly scoped feature such as a support assistant, Q&A interface, or summarization service over a defined corpus.
- Hardening (weeks 6–10). Refine prompts, retrieval, guardrails, and monitoring based on real traffic and feedback.
- Scale (10+ weeks). Extend to additional teams, countries, or product lines using the same underlying platform.
Throughout the engagement you get direct access to senior engineers and product leads, consistent communication, and documentation you can share internally with leadership and security.
You do not need a perfect AI roadmap to start. Bring your use cases, constraints, and a clear picture of what "good" looks like for your team.
Start a project or request an LLM integration review and we will respond with a concrete, plain-English plan with models, timeline, and likely ROI that you can take back to your stakeholders.
