LLM Integration Services
Language models wired into the product and data you already haveUseful large language model integration starts inside your existing product. PixelPlex connects a model to the data and interfaces your team already maintains, defines what it may read or trigger, and tests the output against real scenarios.
Challenges we solve
Answers that ignore what your company knows
A general model has never seen your pricing rules or support history, so questions about them get plausible guesses. Retrieval from approved internal sources supplies the missing context and links every answer to the document behind it.
A promising prototype stuck outside real systems
The demo worked, then stalled. It reads from a static file, has no login, and cannot reach the ticketing or order system where work happens. Production integration adds authentication, live data connectors, and error handling for each call.
Confident answers with wrong facts inside
Fluent text hides errors well. A model may quote a nonexistent policy clause or mix up two customers. Grounding answers in retrieved sources and checking citations automatically exposes those errors before anyone acts on them.
Access rights nobody mapped to the model
An assistant that indexes every shared drive can hand an intern the salary spreadsheet. Retrieval filters results by each user’s rights, and sensitive fields are masked before reaching the model.
Latency and token bills that grow with every user
Twenty pilot users hide costs that a thousand users expose. Long prompts, repeated context, and one large model for every request add seconds and dollars. Caching, shorter prompts, and routing simple tasks to smaller models control both.
Quality that drifts after a quiet update
Someone edits a prompt to fix one complaint, and three other scenarios break. Provider model updates do the same. A regression suite of real queries runs before each change ships, so the team sees the difference first.
Our LLM integration services
Each service below covers one layer of large language model integration, from choosing a model to connecting it with systems that act on its output. Projects often start with one layer and add others later.
Integration assessment & model selection
Before any code, we map the scenario, the data it depends on, and the systems involved. Candidate models are then compared on your own tasks, and the hosting choice follows from privacy, latency, and budget requirements.
LLM API & application integration
Calling a model API takes minutes. Keeping that call reliable inside a live product takes engineering: context assembled from the right records, retries within rate limits, and a fallback path so a provider outage affects one feature only.
Enterprise knowledge & RAG integration
Retrieval-augmented generation lets the model answer from your documents without training on them. Search respects each user’s access rights, the index refreshes when sources change, and every answer shows which file or record it came from.
Model adaptation & fine-tuning
Fine-tuning helps when a task needs a consistent format or domain vocabulary that prompting cannot deliver. We first test whether better prompts or retrieval solve the problem. If they fall short, adaptation runs on a curated example set.
Model evaluation & optimization
An evaluation set of real queries turns “seems better” into a measured comparison with the baseline. The same set guides optimization, such as shorter prompts or a smaller model, and shows when savings start hurting quality.
Enterprise workflow & system integration
When the model must act, say, by updating a CRM record or an ERP order, it calls only approved API endpoints. Each call is validated against a schema, and the result is checked before the workflow moves on.
Success stories
The four projects below come from adjacent PixelPlex work in AI, data processing, and system integration. Each had to handle real inputs, such as shopper movement in a mall or retina scans, and fit an existing business process.
AI shopper analytics with iBeacon technology
- Retail
- AI analytics
- iBeacon
- Data visualization
- Mobile app
Smart Mall gives retailers visibility into in-store shopper behavior. PixelPlex built the AI analytics logic, integrated iBeacon hardware, and delivered a retailer app that tracks and visualizes the data. It was tested in two large retail locations.
- AI logic for shopper behavior analytics
- iBeacon technology integration
- Mobile app for retailers
- Data tracking and visualization
- Testing in two large retail locations
AI-based shared grocery shopping list app
- Retail
- Mobile app
- Deep learning
- Recommendations
- QA
Kooper lets several people shop from one shared grocery list. PixelPlex implemented a deep learning model that recommends products based on shopping habits, integrated product and discount data, and covered QA and support for the app.
- Collaborative shopping list mobile app
- Deep learning model implementation
- Personalized product recommendations
- Product and discount data
- QA and ongoing support
AI analyzer of retina disease symptoms
- Healthcare
- Medical imaging
- Neural networks
- Data preparation
- Web app
AIRA analyzes retina images to recognize disease symptoms. PixelPlex prepared and normalized the image data, trained neural networks for symptom recognition, and built a medical database with a web interface for internal testing.
- Retina image processing
- Data preparation and normalization
- Neural network training for symptom recognition
- Medical database
- Web interface for internal testing
Warehouse automation concept for an omnichannel hypermarket
- Retail
- Warehouse automation
- Digital twins
- Prototyping
- Technical specifications
An omnichannel hypermarket needed a warehouse automation plan before committing to equipment. PixelPlex developed the concept and built a visualization prototype with digital twins. Hardware was selected together with suppliers, and technical specifications were prepared ahead of procurement.
- Warehouse automation concept
- Visualization prototype with digital twins
- Equipment selection with suppliers
- Technical specifications
- Preparation up to the procurement stage
Why work with us
Integration that respects your existing product
Most teams arrive with a live product, an established stack, and release rules that cannot pause for an AI experiment. LLM integration work fits those constraints, from your authentication model to your deployment pipeline and existing API contracts.
Quality measured on your scenarios
Public benchmarks say little about how a model handles your contracts or support tickets. Evaluation runs on test sets drawn from your own data, with pass criteria agreed in advance, so launch decisions rest on measured results.
A solution your team can maintain
At handover, your engineers receive the code, evaluation suites, and operations runbooks. They also get a documented process for updating prompts, swapping models, and refreshing indexes, so future changes do not depend on PixelPlex.
$1.2B+
raised by our clients
$5M
in first-year revenue growth
10M+
users scaled in the first 18 months
3
unicorns among our projects
450+
projects completed
50M
happy end users for our clients
Top Clutch AI Deployment Company New York City 2026
Top Clutch AI Consulting Company Warsaw 2026
Top Clutch AI Security Management Company Warsaw 2026
Top Clutch Identity and Access Management New York City 2026
Top Clutch BI & Big Data Company New York City 2026
Key benefits of LLM integration
1.
Answers inside the tools people already use
Support agents stop switching between five tabs to find a return policy. The answer appears in the helpdesk sidebar, drawn from current documentation, and the agent can open the source with one click before replying.
2.
Less manual work on repetitive text tasks
Summarizing call notes, sorting inbound emails, and filling fields from PDFs eat hours every week. A model can prepare first drafts of that work, leaving people to review, correct, and approve instead of starting from a blank screen.
3.
Responses grounded in approved company sources
Employees can see where an answer came from and judge it accordingly. Retrieval draws only from sources your team has approved, so an outdated wiki page or a draft policy does not quietly shape what the assistant says.
4.
Model actions you can audit
When a model files a ticket or updates an order, the action stays inside a defined workflow. Every call is logged with its inputs and result, so a reviewer can trace each step and spot errors.
5.
Spending you can see and a model you can replace
Token usage is tracked per feature, so finance and engineering see which workflows cost the most. A provider-neutral interface layer means a cheaper or better-suited model can be tested and swapped in without rewriting the product.
Cost of LLM integration
Starting at
$15,000
The estimate is set after discovery. It mostly depends on how many systems the model connects to and how much data needs preparing. Strict access requirements and deeper quality testing add work as well.
What’s included:
- Use case assessment and architecture
- Core model integration
- Data preparation or RAG, per the agreed scope
- Testing and initial deployment
Model API consumption, cloud infrastructure, and extended support depend on actual usage and the agreed scope, so they sit outside this estimate. Discovery includes a usage forecast, making those running costs visible before development starts.
LLM integration across industries
Every industry has documents, rules, and systems a model must respect. The scenarios below show where large language model integration fits, with the model handling language while source systems keep control of numbers, records, and final decisions.
FinTech
Analysts spend hours searching past research before writing a market note. A retrieval assistant finds internal reports and drafts a review citing each source. Figures come from the data platform, and the analyst edits the final text.
- Internal research search
- Cited draft market reviews
- Earnings call summaries
- Analyst Q&A over reports
Banking
Branch staff need the current procedure for a rare case, such as reopening a dormant account. An internal assistant answers from policy documents each employee is cleared to see and drafts client messages for review before sending.
- Procedure Q&A for staff
- Role-based document access
- Client message drafts for review
- Policy change summaries
Payments
Many “where is my transfer?” tickets can be answered from processor data. The model sorts incoming requests, reads the payment status through an API, and explains it in plain language. It never initiates or changes a payment.
- Support ticket classification
- Payment status explanations
- Dispute case summaries
- Merchant FAQ assistant
Insurance
Claims handlers read long files to find a few facts: dates, amounts, damage descriptions. Extraction pulls those fields from submitted documents into the claims system, and policy search surfaces the coverage terms relevant to each case.
- Claim document field extraction
- Policy terms search
- Adjuster file summaries
- Correspondence drafts for review
Lending
An incomplete application can sit in a queue before anyone notices the missing pay stub. The model checks each package against the required document list and prepares a summary for the loan officer, who makes the credit decision.
- Application completeness checks
- Borrower document extraction
- Loan officer summaries
- Applicant status updates
Retail & eCommerce
Shoppers ask whether a jacket comes in medium and ships by Friday. The assistant checks live catalog and inventory data through store APIs before answering. Merchandisers also get product description drafts built from supplier specs.
- Catalog and availability Q&A
- Product description drafts
- Order status assistant
- Review and feedback summaries
Supply chain & logistics
Bills of lading and customs forms arrive as scans in dozens of layouts. Field extraction turns them into structured records for review. Delivery questions from customers get answers built on tracking data from the connected TMS.
- Transport document extraction
- Delivery status Q&A via TMS
- Exception report summaries
- Carrier email triage
Healthcare
Hospital staff lose time hunting for the approved version of a protocol or discharge template. Search limited to vetted materials helps them find it, and administrative document processing runs with patient identifiers masked and every access logged.
- Approved protocol search
- Administrative document processing
- Referral and intake summaries
- Masking of patient identifiers
Fitness
Members want to know why a program schedules squats on Monday or which exercise replaces a lunge. The assistant explains programs and searches the exercise library, and declines medical questions by pointing users to a qualified professional.
- Training program explanations
- Exercise library search
- Coach message drafts
- Guardrails for medical topics
Education
Students ask the same questions about deadlines, grading rules, and course materials every term. Connected to the LMS, an assistant answers from the institution’s published policies and the materials instructors have uploaded for each course.
- Course material Q&A
- Academic policy answers
- Study guide drafts
- LMS integration
Real estate
Agents field vague requests like “a quiet two-bedroom close to transit.” The model turns that into structured filters for the listing database. It also extracts key terms from lease agreements, and an agent confirms each extracted clause.
- Natural-language listing search
- Lease term extraction
- Listing description drafts
- Human review of extracted terms
Construction
A single design revision can touch drawings, specs, and RFIs across several folders. Search across project documentation finds what changed, and the model drafts a change summary the site team can read before the morning briefing.
- Project document search
- Change summary drafts
- RFI and submittal Q&A
- Daily report summaries
Oil & gas
Technicians in the field need the right step from a 400-page manual, fast. The assistant searches technical instructions and summarizes maintenance logs for specialists. Equipment control stays with operational systems, and the model only reads and reports.
- Technical manual search
- Maintenance log summaries
- Safety procedure Q&A
- Shift handover notes
Restaurant
Guests ask about gluten-free options, corkage rules, or a table for six on Saturday. Menu and policy questions get answered from current data, while availability checks and bookings go only through the reservation system’s API.
- Menu and dietary Q&A
- Booking policy answers
- Reservations via system API
- Guest review summaries
Travel
A traveler asks whether a connection in Frankfurt allows enough time and what the baggage rules are. The assistant helps plan routes and explains supplier policies, while fares and seat availability come only from connected booking systems.
- Itinerary planning help
- Supplier policy explanations
- Fares from connected systems
- Booking change assistance
Entertainment
Large content libraries are hard to search by mood or theme. Semantic search lets users and editors find titles by description. For moderation teams, the model sorts incoming reports by category, and moderators make the final call.
- Content library search
- Moderation report classification
- Metadata and synopsis drafts
- Fan support assistant
Our process
1. Discovery & integration assessment
2. Architecture & model selection
3. Prototyping & feasibility validation
4. Application & retrieval engineering
5. Evaluation & performance optimization
6. Testing, deployment & handover
LLM integration technology stack
Tools are chosen per project. Answer quality and data residency rules narrow the options first. The existing product’s architecture and the hosting model, whether a vendor API or your own infrastructure, settle the rest.
OpenAI API
Anthropic Claude API
Google Gemini API
Meta Llama
LangChain
LlamaIndex
PostgreSQL with pgvector
Qdrant
Python
FastAPI
TypeScript
Node.js
PyTorch
Hugging Face Transformers
Amazon Bedrock
Azure OpenAI
Google Cloud Vertex AI
Docker
Kubernetes
Langfuse
OpenTelemetry
Our signature domains
LLM projects rarely stay inside one practice. A language model often sits on top of blockchain data, tokenized asset records, or analytics pipelines, so these four domains share engineers, integration patterns, and review standards.
Blockchain
- DApp and distributed ecosystem development
- ZK rollup scaling solutions
- Cryptocurrency exchange development
- Smart contract engineering and audit
- Web3 development
Tokenization
- Physical and digital asset tokenization
- NFT development services
- Carbon credit tokenization
- Security Token Offering (STO) development
Your journey with PixelPlex starts here
STEP 1
Reach out, no pressure
- Drop us a line, call, or fill out our form.
STEP 2
Deep dive: consultation
- Let’s discuss your goals, budget, and timeline.
STEP 3
Project plan & estimate
- Receive a clear roadmap, scope of work, and investment estimate.
STEP 4
Kickoff & development
- Once aligned, we’ll sign the agreement and launch your project.
FAQ
How can we tell whether a use case needs an LLM at all?
Start with the input. If the task depends on understanding unstructured text, such as emails or contracts, a language model may fit. Fixed rules, exact calculations, or simple lookups are usually cheaper and more reliable with conventional code.
How does the model work with our own data, and when is RAG needed?
RAG retrieves relevant passages from your sources at query time and adds them to the prompt. It fits when answers depend on changing internal documents. The model is not retrained on that data, and access rules still apply.
How do you keep sensitive data away from the model?
Several layers work together. Retrieval checks each user’s permissions before fetching documents, sensitive fields can be masked before prompts leave your environment, and hosting options include private cloud deployments. Logs record which data reached the model.
Can you add a model to our product without rebuilding it?
Usually, yes. The model sits behind a new service that your application calls through an API, so existing code changes little. Some products need refactoring at the data layer first, and discovery shows whether yours does.
What drives the cost and timeline of LLM integration?
Scope matters most: the number of connected systems, the state of your data, and the depth of evaluation. Timelines also depend on how quickly your team grants access to APIs, test environments, and people who know the workflow.
Services
Blockchain
Blockchain consultingStrategy, architecture, feasibility
Crypto wallet developmentSelf-custody, MPC, cards on top
Cryptocurrency exchange developmentTrading venues, liquidity and protocols
Tokenization servicesRWA, funds, collateral
Crypto payments developmentAccept, settle and pay out in crypto
Smart contracts developmentDesign, build, audit
Blockchain integration dApp development DeFi development NFTs Tokens Web3 development All blockchain services All servicesNetworks
Canton developmentDaml apps, wallets, settlement, validators
Official Partner
Ethereum developmentSmart contracts, L2s and dApps
Solana developmentHigh-throughput programs and dApps
TON developmentMini apps, wallets, Telegram ecosystem
Official Partner
Hedera developmentHashgraph apps and token services
Exclusive Partner
Hyperledger developmentPermissioned networks for enterprises
Certified Partner
Cardano Algorand Stellar Avalanche Bitcoin Polygon All networks All servicesAI & ML
AI agents developmentConnect tools and automate multi-step workflows
RAG developmentFind answers across your company’s documents and systems
AI copilot developmentAdd useful assistance to your product or internal tools
LLM developmentCustom and fine-tuned models for your domain
Private AI deploymentRun models in your own environment
AI consultingUse cases, feasibility and a roadmap
Generative AI development Machine learning development Computer vision Chatbot development ChatGPT integration Deep learning development All AI services All servicesData & analytics
Data engineeringPipelines that keep your data reliable
Data platform developmentOne source of truth for analytics
Analytics & dashboards developmentBI your teams actually use
Predictive analyticsForecasts built on your own history
Data strategyWhere data can pay off first
Data visualizationDashboards and visual stories from your data
Data science development Development of data marts All data services All servicesSoftware
Product discoveryValidate scope and cost before you build
MVP developmentA launch-ready first release
Web application developmentPlatforms, portals and internal tools
Mobile app developmentiOS, Android and cross-platform
SaaS developmentMulti-tenant products that scale
Software integrationConnect the systems you already run
Infrastructure & cloud cost UI/UX design IT consulting QA & software testing R&D Serverless development All software services All servicesSecurity & compliance
Smart contract auditsFind vulnerabilities before launch
Transaction monitoring developmentIntercepta inside your systems and processes
Security assessmentsTest apps, infrastructure and processes
Data security servicesProtect sensitive data across its lifecycle
Cloud security servicesSecure cloud workloads and configurations
DAML smart contract auditReview DAML contracts before they go live
All security services All servicesSolutions
Products, APIs and platforms
CC SuiteNew
Products and engineering to build and operate on Canton.
In production 55M API requests served in 8 months
Crypto APIBlockchain data for your applications.
Power in numbers 6 TB Data storage. 500K Tokens indexed
RWA platformA foundation for your asset tokenization platform.
Time to launch 2–4 weeks Branding & setup. 90% more cost-efficient
DocFlowBlockchain-based document management.
Use case & value 100% Secure workflows. Zero third-party risks.
OTC Hawk Arbitrage Bot Book a solution consultWork
450+ projects. 250+ clients. 3 unicorns in portfolio
Console walletThe first non-custodial wallet built natively for Canton
TON ecosystemA series of improvements for The Open Network
Mining platformEnvironmentally friendly web platform for Bitcoin pool mining
Mantra DAOCommunity-governed DeFi: 60+ staking pools, $23M TVL
Canton TON Web3 & DeFi Digital Assets & Tokenization AI & Data Intelligence IoT & Edge Computing Consulting See all projectsIndustries
Trusted by startups, small businesses, and enterprises
Financial services software developmentBanking, payments, lending, insurance, capital markets
Real estate software developmentInvestment, property operations, tokenization
Retail & commerce software developmentCommerce, loyalty and fulfillment
Supply chain software developmentTraceability and connected operations
Healthcare software developmentPatient services, data and connected systems
Banking software developmentCore banking, digital channels, payments
Fitness Education Oil & Gas Insurance Construction TravelCompany
From R&D to launch, we bring any of your ideas to life
About PixelPlexEngineering partner. Product builder. Since 2007.
Technical experience 19+ years All competencies on board. Scalable team.
AchievementsIndustry recognition & awards.
Delivering real value 4.9 Clutch ratings. 150+ awards
Contact usDiscuss your project.
Quick response 30 min Average reply time. Offices in 6 countries
BlogDive into the latest software trends.
Fresh insights 500+ Articles in blockchain, AI, big data, AR/VR
Team Careers Social responsibility Glossary