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FIG 01 · LLM Integration

LLM development and ChatGPT integration services

ChatGPT, Claude, Gemini or open-weight models built into your product and internal tools, grounded in your data, tested with evals and watched in production.

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See how it works
Machine-learning engineer reviewing an AI chat interface and a system diagram on two monitors

FIG 02 · Quick answer

What does an LLM development company do?

Elgrith is an LLM development company that builds large language model features into products and internal tools. We integrate ChatGPT, Claude, Gemini or open-weight models, ground answers in your data with retrieval-augmented generation (RAG), add evals and guardrails, and monitor cost and latency in production. We designed and engineered Kenerate AI.

AI models behind Kenerate AI, our flagship build
200+
Projects delivered since 2015
1,000+
Clients across the USA, UK and India
250+
Your code, data and admin access
100%

FIG 03 · Why it matters

Cheaper models. Clearer data rules. Fewer excuses.

From OpenAI’s developer docs and Stanford HAI’s AI Index 2025. The cost and data questions that used to stall LLM projects now have answers.

FIG 04 · Quick answer

RAG or fine-tuning?

Most business use cases need good prompts and RAG, not a custom model. Here is when each approach fits.

Prompting, RAG, fine-tuning or a custom model
ApproachBest forWhat it needsWatch out for
Prompt-onlyDrafting, rewriting, classifying and summarising general textClear prompts, structured outputs and a test setIt knows nothing about your private data
RAG (retrieval-augmented generation)Answers from your documents, products or records, with citationsClean source data, embeddings, a vector index and access rulesAnswers are only as good as retrieval, so it needs evals
Fine-tuningA consistent format, tone or narrow task at high volumeA set of high-quality example inputs and outputsIt doesn’t add fresh knowledge; retrain when things change
Custom modelRare cases with unique data at large scale or strict hosting rulesLarge datasets, ML infrastructure and a long runwayHighest cost and slowest to change

We often combine them: RAG for knowledge, structured outputs for format, and fine-tuning only when evals show it helps.

FIG 05 · What you get

LLM development services

Model-agnostic engineering, from the first prompt to production monitoring. We pick the model per task and prove it with evals.

Tablet showing an AI chat beside a sketch of documents flowing through a database into answers
LLM Integration · built in-house

01

ChatGPT and OpenAI API integration

GPT models wired into your product through the OpenAI API, with function calling, structured outputs and streaming responses.

02

Claude, Gemini and open-weight models

Anthropic Claude, Google Gemini, Llama or Mistral, chosen per task on quality, cost, latency and hosting needs, behind one interface you can switch.

03

RAG development on your data

Retrieval over your documents, help centre, CRM or database with embeddings, pgvector or Pinecone, permission-aware search and cited answers.

04

Structured extraction and AI search

Invoices, contracts, emails and forms turned into clean fields, and semantic search that understands what people mean, not just keywords.

05

MCP servers and ChatGPT apps

Model Context Protocol servers and apps built with the ChatGPT Apps SDK, so Claude, ChatGPT and other AI tools can use your product safely.

06

Evals, guardrails and monitoring

Test sets and evals before launch, prompt-injection and PII guardrails, and tracing with Langfuse, LangSmith or OpenTelemetry for cost and latency.

Deliverables

12

deliverables, in writing

Included in every build, written into your fixed quote before work starts.

FIG 06 · Use cases

Where LLMs earn their keep

The work that pays back fastest: reading, sorting, finding and drafting, with a person in charge of the decisions.

  • Documents
  • Database
  • CRM records
  • Support tickets
  • Product features
  • Internal tools
  • AI search
  • Reports

  • Answers from your knowledge base

    Input
    Policies, manuals and help articles
    LLM step
    RAG with citations and access rules
    Output
    Answers staff can check at the source

  • Document extraction

    Input
    Invoices, contracts and forms
    LLM step
    Structured outputs against a schema
    Output
    Clean fields in your system, flagged when unsure
    AI Automation

  • A copilot inside your SaaS

    Input
    The user’s own data in your product
    LLM step
    Drafts, summaries and suggestions
    Output
    A feature customers pay for
    SaaS Development

  • CRM summaries and lead research

    Input
    Calls, emails and CRM records
    LLM step
    Summaries and next-step suggestions
    Output
    Less admin for sales, with a person approving
    Zoho Consulting

  • Support reply drafting

    Input
    Incoming tickets and past resolutions
    LLM step
    Classify, route and draft a reply
    Output
    Agents edit and send, not start from blank

  • Your product inside Claude and ChatGPT

    Input
    Your product’s API
    LLM step
    An MCP server or ChatGPT app with scoped permissions
    Output
    Customers use you from their AI tools
    AI Agent Development

FIG 07 · Flagship build

Built by the team behind Kenerate AI

Kenerate AI is our flagship build, an all-in-one AI image and video studio we designed and engineered. It runs many AI models behind one product, the same kind of model-agnostic engineering your LLM features need.

  • 200+ AI models behind one app, one login and one credit balance
  • Kenerate Agent: chat with an agent that picks the model and runs the workflow
  • MCP & CLI: use Kenerate inside Claude, ChatGPT, Cursor and 19+ AI apps
  • 23+ studios and tools, from Image Studio to Director
See the case studyVisit kenerateai.com (opens in a new tab)
Kenerate AI agent page: Kenerate inside Claude, ChatGPT and Cursor through MCP and CLI

Agent: Kenerate inside Claude, ChatGPT and Cursor

AI models in one app
200+
Studios and tools
23+
Maximum output
4K
Guides
500+

FIG 08 · Data and providers

Your data and model providers

Every provider has its own data terms. We read them for your use case and design the data flow before any production data is sent.

05principles

  • Training use

    OpenAI says data sent to its API has not been used to train or improve its models since March 1, 2023. We check the equivalent terms for every provider we propose.

  • Retention

    OpenAI keeps abuse-monitoring logs for up to 30 days by default. Where a provider offers zero-retention options and your use qualifies, we help you apply for them.

  • PII redaction

    Personal data masked or removed before it reaches a model, when the task doesn’t need it.

  • Permission-aware retrieval

    RAG only retrieves what the signed-in user is allowed to see, so the model can’t leak another team’s records.

  • UK GDPR and the EU AI Act

    We document data flows, providers and model use to support your DPIA and risk assessment. Your legal adviser confirms your obligations.

FIG 09 · Related builds

Chatbots, agents and automation

LLM integration is the engine. If you need a specific vehicle, these pages go deeper.

06principles

FIG 10 · Measurement

How we know the LLM feature works

We agree what “good” means before building, then measure it on every release.

The LLM quality scorecard
MetricWhat it tells youHow we measure it
Answer accuracyWhether outputs are correct for real casesAn eval set of your own questions and expected answers
GroundednessWhether answers stick to your sourcesCitation checks and LLM-as-judge reviews, spot-checked by people
RefusalsWhether it declines what it shouldn’t answerOut-of-scope and prompt-injection test cases
LatencyHow long users waitTraces per request in production
Cost per taskWhat each answer or extraction costsToken and provider usage per feature
User feedbackWhether people find it usefulThumbs, edits and escalation rates

Targets are set with you during the prototype. We don’t quote accuracy figures before testing on your data.

Next step

Need it to take actions, not just answer?

When the model should look things up, update records and hand off to people, you need an agent with tools, permissions and approvals.

AI agent development

FIG 11 · Process · 6 steps

Prove it with evals, then ship

A fixed written quote and a phased plan. You see the prototype’s eval results before we build for production.

Book a call
  1. Step 01

    Use case and metric

    The task, the users, the data involved and the success metric, written down. You get a written scope and a fixed quote.

  2. Step 02

    Data review

    We check the documents and records the feature will use: quality, permissions, personal data and how often they change.

  3. Step 03

    Prototype with evals

    A working prototype tested against an eval set built from your real cases, comparing models and approaches.

  4. Step 04

    Production build

    The feature built into your product or tools, with guardrails, access rules, fallbacks and tracing.

  5. Step 05

    Launch and monitor

    A staged rollout with dashboards for quality, cost and latency, and alerts when something drifts.

  6. Step 06

    Improve

    Evals rerun on every prompt or model change, and new cases added from real usage.

FIG 12 · What you get

Everything included

Written into your fixed quote, so you know exactly what you are paying for before work starts.

12items

  • Use-case brief and success metric
  • Written scope and fixed quote
  • Data review and data-flow diagram
  • Model comparison with eval results
  • Prompts, retrieval pipeline and schemas
  • Guardrails for PII and prompt injection
  • Production integration in your product
  • Tracing, cost and latency dashboards
  • Eval set you can rerun
  • Provider and data-handling notes for your DPIA
  • Documentation and handover
  • All code, prompts and data, yours

FIG 13 · Tech & tools

The tools behind the work

Proven, well-supported tools, chosen for your project and set up in accounts your business owns.

  • OpenAI API
  • Anthropic Claude
  • Google Gemini
  • Llama
  • Mistral
  • Structured outputs
  • Function calling
  • Model Context Protocol
  • ChatGPT Apps SDK
  • Embeddings
  • pgvector
  • Pinecone
  • PostgreSQL
  • Langfuse
  • LangSmith
  • OpenTelemetry
  • Python
  • Node.js
  • Next.js

FIG 14 · Who it’s for

LLM features for every kind of team.

Same engineering, different starting points.

3 audiences

SaaS

Adding AI to a product customers pay for.

LLM features that fit your product, your pricing and your users’ data, with costs you can predict.

  • Model choice per feature
  • Usage and cost per tenant
  • Permission-aware retrieval
  • Evals in your release process

FIG 15 · Working with us from the USA and UK

Built in New Delhi. Run on your hours.

Every project gets a point of contact in New York or London and an engineering team in New Delhi. Here is how today’s working days line up, live.

Three ways to work together

  1. 01

    Fixed-scope project

    A written scope, one fixed price and a dated plan. Suits websites, apps and MVPs.

  2. 02

    Dedicated team

    Designers and engineers on your roadmap every month, billed monthly.

  3. 03

    White label

    For US and UK agencies: our work, your brand, unbranded deliverables.

Every engagement

  • Fixed quote in USD, GBP or INR after a free 30-minute call
  • NDA before discovery. You own all code, designs and content
  • A working demo every sprint, not status slides
  • Reply within one business day, calls inside your hours
Book a call

Calls on ET or UK hours

FIG 16 · Model-agnostic

Which model family fits?

We don’t sell one provider. These are the usual starting points, and evals on your data make the final call.

Which model family fits?
Model familyOften a good fit whenConsider
OpenAI GPT modelsYou want a broad ecosystem, structured outputs or a ChatGPT appData terms and retention for your use case
Anthropic ClaudeTasks involve long documents, careful writing or MCP-based toolsProvider terms and regional availability
Google GeminiYou already run on Google Cloud or Workspace, or need mixed media inputWhich Gemini offering and terms apply
Open-weight (Llama, Mistral)You need self-hosting, data residency or cost control at volumeHosting, scaling and quality trade-offs

FIG 17 · Compare

LLM integration, AI chatbot or AI agent?

LLM integration, AI chatbot or AI agent?
LLM integration (this page)AI chatbotAI agent
What it isLLM features inside your product or internal toolsA conversational assistant for customers or staffSoftware that plans steps and uses tools to finish a task
Typical outputExtracted fields, search results, drafts, summariesAnswers in a chat window, with handoverUpdated records, sent drafts, completed workflows
Where it livesYour app’s screens, APIs and back officeWebsite, WhatsApp, Slack or phoneBehind the scenes, with approvals
Start here ifYou want AI inside what you already haveYour customers ask the same questions dailyA multi-step task eats hours every week

FAQ

Frequently asked questions

Straight answers about LLM Integration with Elgrith.

An LLM development company builds features on large language models: it picks the right model, connects it to your data with RAG or APIs, writes and tests prompts, adds guardrails and evals, and runs the feature in production with cost and latency monitoring. Elgrith does this for products and internal tools, and designed and engineered Kenerate AI.

FIG 18 · Related

Keep exploring

All services

Free 30-minute call

Put an LLM to work on your data.

Tell us the task and the data behind it. After a free call you get a written scope, an eval plan and a fixed quote.

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