Generative AI Development Services
We build custom generative AI applications on your data, from architecture through production. Senior US-based engineers, no wrappers, no retrofits.
Empowering teams at leading organizations worldwide
Generative AI Development Services Across the Stack
We build across the full generative AI development spectrum, from foundation model integration to production deployment. Select a capability to see what we build and how we engineer it.
Generative AI Applications
We build generative AI applications that run on your proprietary data, not generic model knowledge, with prompt engineering and hallucination guardrails designed in from sprint one.
Key Benefits & Outcomes
- Grounded in your data
- Prompt engineering for accuracy and cost
- Output validation and guardrails built in
- Model customization to your use case
Technologies & Process
We select the model approach before building: fine-tuning, RAG, in-context prompting, or a hybrid, chosen against your accuracy needs, data sensitivity, and cost per output. We build on OpenAI, Anthropic, Google Gemini, AWS Bedrock, and open-weight models, orchestrated through LangChain and LlamaIndex. Prompt versioning, structured outputs, and function calling are engineered into the application layer, and inference cost controls are set before launch. Every build ships with an evaluation harness, so accuracy is measured against a defined benchmark at each sprint rather than assessed once at go-live.
RAG & Knowledge Systems
Retrieval-augmented generation grounds answers in your knowledge base, reducing hallucination. We design the data pipeline, vector database, and retrieval architecture before any model is connected.
Key Benefits & Outcomes
- Answers grounded in your documents
- Chunking and embedding tuned for accuracy
- Hybrid semantic and keyword search
- No data sent to external training
Technologies & Process
We build the generative AI data pipeline and vector database first, then tune chunking, embedding strategy, and retrieval for your content before the model is connected. Built on Qdrant, Pinecone, or Weaviate with LangChain and LlamaIndex orchestration. Source traceability is built into every response, so each answer carries the citation an audit or compliance review needs. Retrieval accuracy is validated against a benchmark query set before go-live, and the retrieval layer is monitored in production so relevance holds as your knowledge base grows.
Document Intelligence
We build document intelligence systems that extract, classify, and summarize unstructured content at scale: contracts, forms, reports, and records, against accuracy targets set before the build.
Key Benefits & Outcomes
- Extraction and classification at scale
- Summarization grounded in source text
- Automated report generation with traceability
- Accuracy benchmarks set upfront
Technologies & Process
We design the ingestion, preprocessing, and metadata-enrichment pipeline before selecting a model, so the system meets the documents it will actually see in production rather than a clean sample. Extraction, classification, and summarization are built against a defined evaluation harness, with structured outputs and function calling routing results into your existing systems. Built on OpenAI, Anthropic, and open-weight models with LangChain orchestration. Human evaluation and factual-consistency testing run at every sprint, and each output traces back to its source text.
Multimodal AI
We build multimodal AI applications across text, image, and structured data in one system: visual question-answering, image-grounded generation, and cross-format search, scoped to the modalities you need.
Key Benefits & Outcomes
- Text, image, and data in one application
- Visual question-answering and outputs
- Cross-format semantic search
- Modality scope matched to use case
Technologies & Process
We define which modalities create real leverage before building, so the system carries only the complexity the use case justifies. We then design the inference pipeline and model routing that handles each modality, built on multimodal foundation models from OpenAI, Google Gemini, and Anthropic, with a multi-model architecture where a single model cannot cover the full use case. Accuracy is benchmarked per modality rather than as one blended score, and cost per output is modeled before any build commitment.
Foundation Model Integration
We integrate foundation models into your applications through a designed API and orchestration layer, architected for cost control and scale from the start.
Key Benefits & Outcomes
- Integration into existing applications
- Model API integration with cost controls
- Orchestration across providers
- Model routing for accuracy and cost
Technologies & Process
We design the inference pipeline and orchestration layer before wiring in any model, so generation stays controllable, observable, and swappable as the model landscape shifts every few months. Built on OpenAI, Anthropic, Google Gemini, AWS Bedrock, and open-weight models with LangChain and LlamaIndex. Token optimization, model routing, and context management are engineered into the integration, so each request runs on the model that fits it on accuracy and cost. Rate limits, retries, and fallback paths are handled at the orchestration layer.
Fine-Tuning & Customization
We fine-tune and customize foundation models on your data when prompting and retrieval are not enough, so the model performs on your domain, tone, and task.
Key Benefits & Outcomes
- Model customization on your domain data
- Fine-tuning where prompting falls short
- Training data preparation and evaluation
- Open-weight options for cost and privacy
Technologies & Process
We first test whether prompting or RAG meets the accuracy target, because fine-tuning is only worth its cost and maintenance when it clears a bar the cheaper approaches cannot. Where it is justified, we prepare and validate the training data, run the fine-tune, and evaluate the result against the base model on your specific task. We work across commercial and open-weight models, including private deployments where data sensitivity or cost at scale rules out a hosted API. Every customized model ships with an evaluation harness and drift monitoring, so performance is tracked after launch, not assumed.
Prompt Engineering & Evaluation
We engineer and evaluate the prompt layer that determines whether a generative system is accurate and reliable, treating prompts as versioned, tested components, not throwaway text.
Key Benefits & Outcomes
- Prompt engineering and prompt optimization
- Versioned, testable prompt components
- Hallucination testing before launch
- Continuous evaluation against a baseline
Technologies & Process
We treat prompts as engineered artifacts: versioned, reviewed, and tested against a defined evaluation set rather than tuned by feel. We build the evaluation harness that measures output quality, response relevance, and factual consistency, and run hallucination testing on the cases where a wrong answer carries real cost. Prompt optimization and structured outputs are tuned against those benchmarks, and the same harness runs continuously after launch, so a model update or a data shift that degrades quality is caught before your users feel it. This is the layer most vendors skip and the one that decides production reliability.
GenAIOps & Deployment
We take generative AI to production and keep it there, with deployment pipelines, continuous evaluation, and observability designed alongside the build from sprint one.
Key Benefits & Outcomes
- Deployment with CI/CD pipelines
- Continuous evaluation and monitoring
- AI observability live at launch
- Model versioning and feedback loops
Technologies & Process
We build deployment pipelines, evaluation harnesses, and monitoring instrumentation as part of the product, not as a phase bolted on before launch. Continuous evaluation tracks output quality, factual consistency, and response relevance against a baseline, so a regression is caught the moment it appears. Observability dashboards surface drift, latency, and cost as they move, and model versioning with feedback loops lets you roll a new version out and back without disruption. Built on AWS SageMaker with cost optimization tracked continuously.
We Build Generative AI You Won't Have to Rebuild Next Year
Generative AI Development Company Recognized for Delivery
10k+
AI automation ecosystems ecosystems
65%
AI automation ecosystems ecosystems
10k+
AI automation ecosystems ecosystems
65%
AI automation ecosystems ecosystems
Flexible Engagement Models for Generative AI
Outcome-focused engagements matched to where you are, from first build through ongoing generative AI capability.
Fixed-Scope GenAI Build
A defined generative AI application with a set deliverable list, firm timeline, and price agreed before work begins. Best when scope is clear and leadership needs a committed number before approving budget.
GenAI Proof of Concept
A focused engagement that validates one generative use case against your real data, testing accuracy and cost per output before full investment. You learn whether the core assumption holds before committing to a build.
End-to-End Product Ownership
We own the full lifecycle from architecture through deployment and optimization. One senior team accountable from first sprint to live system, so generative AI moves without pulling your engineers off their existing roadmap.
Embedded GenAI Team
A dedicated senior team aligned to your roadmap and KPIs over the long term. For organizations treating generative AI as an ongoing capability rather than a one-time project, with the same engineers accountable at every phase.
GenAI Feature Sprint
A short engagement to add one generative feature to an existing product: a copilot, a document intelligence tool, or intelligent search, scoped, built against accuracy targets, and shipped without a full product rebuild.
Optimization & Ops Retainer
Post-launch, we keep generative AI production-ready: continuous evaluation, drift and hallucination monitoring, prompt tuning, and cost optimization, so the system holds accuracy and spend as your data and traffic grow.
Powering Progress Across Your Industries
Generative AI built around the compliance, data, and workflow constraints of the industries we serve.
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HIPAA-compliant generative AI for clinical documentation, patient communication, and record summarization. Built to operate inside the compliance boundaries healthcare organizations require, with audit trails and human review checkpoints on every high-stakes output.
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Generative AI for content generation, adaptive assessment, and knowledge assistants, built with the data governance and fairness controls learning environments require before AI touches student-facing workflows.
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Generative AI for demand forecasting narratives, carrier communication, and exception summaries, surfacing decisions inside the systems your operations team already reads without requiring a new platform or data migration.
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Generative AI for listing generation, document processing, and investment analysis, built where proprietary property data creates competitive advantage that off-the-shelf AI tools cannot replicate.
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Generative AI for document intelligence, underwriting summaries, and compliance monitoring, built with the audit trails and explainability regulated finance requires before any model goes near a production decision.
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Generative AI for product content generation, natural language descriptions, personalized recommendations, and customer intelligence, engineered to perform consistently across large catalogs and high-traffic environments without degrading at scale.
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Generative AI features engineered into existing products: in-app copilots, intelligent search, and LLM-powered reporting, built to the security and compliance standards enterprise procurement teams evaluate before approving a vendor.
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Generative AI for policy document review, claims summaries, and risk narratives, with the explainability and audit controls regulators and actuarial teams expect before automated outputs influence a decision.
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Generative AI for contract analysis, document review, and regulatory monitoring, built with audit trails, access controls, and human review checkpoints the professional obligations of legal environments require.
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Generative AI for maintenance documentation, quality reporting, and supplier communication, built to integrate with existing OT and IT infrastructure rather than requiring a platform replacement.
Why Teams Choose AppVerticals for Generative AI Development
Architecture First
We select the model approach before writing code: fine-tuning, RAG, in-context prompting, or a hybrid, chosen against your accuracy requirements, data sensitivity, and cost per output. That decision is documented and signed off before the sprint plan begins, so the build runs on a deliberate technical choice rather than a default.
The generative AI applications that fail in production almost always share one trait: the model was wired in before the architecture was decided. Prompt engineering, guardrails, cost controls, and observability are not features you add later. They are engineering decisions that have to be made at the start, and we make them before a line of code is written.
Senior-Led Delivery
Every generative AI engagement is led by a senior engineer with production experience in LLM applications, RAG systems, and multimodal AI. The team in your architecture review is the team that writes the code, runs the evaluation, and ships the product, with no account manager relaying updates between you and the people doing the work.
That continuity matters in generative AI development more than in standard software, because the decisions made at architecture stage have to carry through every sprint. When the engineer who designed the system is also the one building it, nothing is lost in translation between what was specified and what gets shipped.
Production Proof
We advise from systems we have built and run in production, not from frameworks we have read. Our own internal operations run on generative AI we built, and the controls we design into your system come from problems we have already solved: hallucinations in live deployments, silent accuracy decline, inference costs that scaled unexpectedly, data leaking through a poorly scoped integration.
That production experience is what makes our architecture recommendations specific rather than generic. When we tell you which retrieval strategy fits your use case or why a particular model approach will cost more than your budget allows at scale, it is because we have made those calls before and seen what happens when they go wrong.
Ongoing Partnership
Generative AI is not a launch-and-leave engagement. The model landscape shifts every few months, production data changes the accuracy picture, and the use cases your team wants to add after launch are often the most valuable ones. We stay engaged through the post-launch window and beyond, with the same senior team who built the system running the optimization, retraining, and iteration cycles.
For organizations building generative AI as a sustained capability rather than a one-time project, we offer embedded partnership retainers that align a dedicated team to your roadmap across strategy, build, and scale, so context built in the first sprint carries all the way through.
Trusted by Clients Across Regulated Industries
Enterprises that automated critical operations with AppVerticals and measured the result.
AppVerticals trained a custom detection model that identifies cabinets, drawers, and fittings in a photograph the customer uploads, then lets new textures and finishes be applied to those segmented areas. We have been very pleased with the accuracy the team achieved on the photos our customers actually send us, which are rarely clean. It has changed how our customers reach a decision about their kitchen.
The application created by AppVerticals scans the pool area, stitches multiple images into a single view, and generates both the measurements and the coping layout using AI and geometric algorithms. Work that took one to three days of templating and CAD now takes under ten minutes on site, and the customer sees the finish on their own pool before ordering. The app is live on both the App Store and Play Store, and we are grateful for the team's work in getting it there.
AppVerticals built an application that plans the trip, identifies a catch from a photograph, and answers questions throughout the day. The team handled the vision model integration and the offline draft functionality well, which matters because connectivity offshore is unreliable. The app is live on both stores and we would recommend AppVerticals to others building AI products.
AppVerticals delivered an AI avatar interface that accepts both voice and video input, analyzes sentiment and emotion, and adjusts its coaching response to match. Responses come back in under two seconds, which is what keeps a roleplay session feeling uninterrupted. We have been very pleased with the result and would recommend AppVerticals to teams building conversational AI.
The system AppVerticals built extracts instructor details, course specifics, participation criteria, and grading breakdowns from syllabi that vary considerably in format. It applies the weighting mathematics to the grading data and generates .ICS events from the course schedule. The team handled the prompt engineering well and we would engage AppVerticals again.
The Proof Is in the Generative AI
We Have Shipped
The Generative AI Stack We Build On
Foundation models, orchestration, and vector databases selected for your use case, not the most hyped option.
How We Build Generative AI Products
Five stages, every engagement, regardless of project size or stack. Each stage has a defined output your team reviews before the next one begins. We do not move to the next stage until the current one is signed off, so the build runs on decisions you have approved rather than assumptions we made on your behalf.
Discovery & Scoping
We map where generative AI creates measurable value in your business, validate that your data can support it, and identify the compliance obligations that apply. You leave with a scoped brief, a feasibility read, and a clear architecture direction before any budget is committed to build.
Architecture & Model Design
We select the model approach, design the data pipeline and orchestration layer, set accuracy and hallucination benchmarks, and build the cost model. Architecture is documented and signed off before the sprint plan begins, so nothing is re-scoped mid-build and the inference bill after launch matches the number you approved.
Sprint-Based Build
We build in two-week sprints, each closing with working, deployable software your stakeholders can run and test. Prompt engineering, retrieval tuning, and guardrails are built and evaluated as we go, so quality is enforced across the build rather than assessed in a single review at go-live.
Evaluation & Testing
We test output quality, factual consistency, and response relevance against defined benchmarks at every sprint closure. Hallucination testing and human evaluation run throughout the build, so accuracy problems surface during development rather than in front of your first users in production.
Deployment & GenAIOps
We deploy through CI/CD pipelines with observability, continuous evaluation, and cost monitoring live from day one. Thirty days of post-launch optimization is included as standard, with long-term retainers available for teams building ongoing generative AI capability beyond the initial launch.
From the AppVerticals AI Team
"Teams treat prompts as throwaway text, which is why generative systems degrade quietly. We version them, test them against an evaluation set, and run the same harness after launch. When a model update shifts behavior, we see it before your users do. That layer decides whether a system holds or slowly stops working."
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