Our generative AI development services build custom solutions on your own data, systems, and workflows. As a generative AI development company, SDLC Corp delivers enterprise copilots, intelligent agents, and multimodal content platforms. We start with one clear use case, then ship a working version your team can test, measure, and scale.
Company-wide delivery experience across client engagements; implementations and models represent different measures of work.
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GENERATIVE AI CAPABILITIES
We cover the full path from first use case to production. We design, build, connect, and maintain LLM-based applications that work with your data and systems. Choose one service or combine several, depending on where your project stands.
We rank use cases by value and feasibility, test them against your data, and design the architecture.
Generative AI consulting servicesWe build the full application: backend services, user interface, and model integration.
AI development servicesWe compare models on your own tasks, then design prompts and evaluation sets.
LLM development servicesWe ingest your documents, index them securely, and connect them to a model that answers with citations.
RAG development servicesWe build agents that call tools, run multi-step tasks, and pause for human approval on sensitive actions.
Agentic AI development servicesWe connect generative AI to the systems you already run, including APIs, CRM, ERP, databases, and identity providers.
AI integration and implementation servicesWe monitor output quality, track cost per request, and manage model upgrades.
MLOps servicesEach solution targets one problem inside an enterprise workflow. Each one runs on your own data and systems, so your team gets a working tool instead of a demo.
We connect wikis, tickets, drives, and chat tools through one secure search layer. Employees ask questions in plain language and receive answers with source links. Role-based permissions help teams find relevant knowledge while keeping restricted information within approved access boundaries.
We build support assistants using your help articles, order records, and ticket history. Customers receive answers to routine questions without waiting for manual responses. Complex requests move to your support team with conversation context, helping staff continue each interaction smoothly.
We build document workflows that process contracts, invoices, reports, and scanned files. Teams extract important fields, summarize information, and prepare draft responses. Reviewed outputs flow into your existing business systems, with validation steps that help staff check accuracy before approval.
We build content workflows that turn approved briefs into drafts, images, and video. Your brand rules and existing assets guide each output. Reviewers approve, revise, or reject generated material before publication, helping teams manage production while maintaining consistent editorial oversight.
Industries
Every sector has its own rules and risks. So each build starts with what that sector must protect, from patient data to product prices.
Clinicians spend hours on notes, and staff answer the same patient questions daily. We build documentation assistants that draft notes from approved inputs, plus internal knowledge tools for protocols. A clinician reviews every draft, and patient data stays inside your privacy controls.
Analysts read long filings, and compliance teams handle heavy paperwork. We build tools that summarize research, support customer queries, and flag gaps in compliance documents. Every output is logged and tied to its source.
Large catalogs need thousands of accurate product descriptions. We build pipelines that enrich catalog data and write copy from verified product specs. We also build shopping assistants. Outputs are checked against the catalog, so prices and specs stay correct and the tone stays on brand.
Technicians lose time searching manuals after a machine fault. We build maintenance assistants that search technical documents and service records. Each answer cites the exact manual or log entry, so engineers can verify it before acting. The assistant never controls equipment.
Operations teams sort through delayed and damaged shipment alerts all day. We build tools that summarize exceptions from tracking and warehouse data, then draft customer updates. They connect to live operational systems, so each summary shows current status.
Production teams need many versions of text, images, and video across markets. We build workflows for ideation, asset creation, and localization. Rights checks and editorial review come before release, which keeps your intellectual property protected.
Agents write listings and answer the same buyer questions again and again. We build assistants that draft property descriptions from listing records and answer enquiries from approved documents. Size, price, and legal terms come only from source records, and a person approves each listing.
Development Process
The work runs in six stages. Each stage ends with a result you can review before the next one starts.
We define your business goal, users, data, and baseline. Then, we agree on success metrics, budget, privacy, and response times to confirm a scoped use case and go or no-go decision.
We select the simplest architecture that meets your goals. Depending on the task, we test RAG, prompts, LLM fine-tuning, or AI agents against your data and requirements.
Our generative AI MVP development starts with one focused workflow using real data. Users test it early, so feedback guides improvements before a wider build.
We connect data sources, retrieval, tools, and application interfaces. Identity-based permissions protect access, while logging makes AI answers and tool actions traceable.
AI application testing checks accuracy, task completion, latency, cost, and security using realistic scenarios. We also test missing data and misleading prompts, improve safeguards, and retest.
LLM deployment includes monitoring, human escalation, and feedback loops. After launch, we maintain quality through model upgrades, regular evaluations, and continuous cost optimization.
Our generative AI work draws on company-wide experience in AI deployment, model development, and enterprise integration across global teams.
Live production systems
Across our AI delivery teams
Shipping at Enterprise scale
Fortune 500 to challengers
Company-wide across 12 industries
Company-wide across 6 continents
Across our global client base
Figures reflect company-wide delivery experience, including AI, machine learning, and broader engineering projects. They are not counts of generative AI projects alone.
Technology
We select LLMs, orchestration frameworks, and deployment platforms for your use case. Then, we connect RAG pipelines, AI agents, and custom applications to your data and existing systems.
OpenAI
Claude
Gemini
Llama
Mistral
Hugging Face
LangChain
LlamaIndex
LangGraph
PyTorch
TensorFlow
NVIDIA
Kubernetes
AWS
Azure
Google Cloud
Security & Governance
Secure generative AI development starts at the design stage, not after launch. So we plan data handling, abuse controls, and audit trails before we write the first prompt.
We apply role-based permissions through your identity system, so users access only approved information. Encryption protects data in transit and at rest. Before development, we agree on retention periods, storage locations, and which providers may process your business data securely.
We ground answers in approved sources and require citations. Realistic quality tests guide evaluation and escalation rules. When information is missing, the assistant declines or requests human review. Ease Pet Vet’s chatbot uses the client’s website and acknowledges unanswered questions.
LLM security separates untrusted content from system instructions and limits tool permissions. We test adversarial prompts, validate outputs, and restrict sensitive actions. Our safeguards address the OWASP Top 10 for LLM Applications, including prompt injection, outputs, and excessive agency risks.
Enterprise AI security requires traceable decisions and clear accountability. We log prompts, sources, tool calls, and outputs with appropriate privacy controls. Approval workflows govern sensitive actions, while the NIST AI Risk Management Framework informs governance reviews, responsibilities, and risk checks.
We define latency targets and monitor production performance as usage grows. Caching, model routing, and token limits help manage spending. Usage dashboards track cost per request, while budget alerts help teams investigate changes and adjust capacity before expenses exceed expectations.
Case Studies
These projects show how SDLC Corp connects AI capabilities with business systems and human review.
REST API connectors link Pulastya AI with CRM, ERP and ServiceNow. On a call, the agent verifies the caller, pulls live order data and opens a prioritized IT ticket.
Project scopeOne voice agent, three business systems and two service workflows.
An AI workflow reads freight documents, matches them to shipment and vendor records and routes uncertain cases for review. After approval, it posts cost lines to Oracle Transportation Management.
Project scopeDocument reading, matching, human review and posting to Oracle TM.
Pulastya AI runs on the client’s existing Twilio numbers. It classifies calls by intent, answers from approved documents and routes the rest to the right team with a transcript.
Project scopeNo number change, approved knowledge sources, rule-based routing and human handoff.
Industry benchmark: labor is up to 95% of contact center costs (Gartner, 2022).
Read the AI Call Intake and Routing Case Study
Pulastya AI answers routine calls from approved documents, captures caller details and hands complex enquiries to staff with full context.
Reported result45% faster call response and 35% fewer missed enquiries.
Our strengths come from systems we have built and still support, both for clients and for our own products.
One team builds lead scoring, enquiry routing, machine insights, and manager reports as one connected workflow. For QuantusTechnik, this approach brought related tasks together with clear ownership throughout project delivery.
For Transworld, we connected invoice extraction to Oracle Transportation Management. As an official Odoo partner, we also bring practical experience connecting enterprise AI with existing ERP systems and business workflows.
We build and run Pulastya AI for voice calls and Data AI Ninja for document processing. Operating these products gives our engineers practical experience with monitoring, escalation, and production support.
We keep code readable and dependencies small, helping your team maintain the system. Ease Pet Vet uses Node.js, Express, and SQL, with scheduled updates keeping its website knowledge base current.
Generative AI development costs depend on scope, model choice, data readiness, and integrations. Compare project budgets, timelines, and engagement models below.
| Project type | Typical market range (USD) | Typical timeline |
|---|---|---|
| Proof of concept | $15,000 to $50,000 | 3 to 6 weeks |
| RAG knowledge assistant | $30,000 to $120,000 | 8 to 12 weeks for an MVP |
| AI agent | $30,000 to $120,000 for a single-purpose agent; $80,000 to $200,000+ for multi-step agents | Varies with scope |
| Custom generative AI application | $80,000 to $500,000, based on RAG, MLOps, and product scope | Varies with scope |
| Enterprise platform with several integrations | $300,000 to $1.2 million+ | 6 to 18 months |
| Running costs after launch | About $1,050 to $3,100 per month for a RAG assistant at 500 requests a day | Recurring |
| Cost factor | What it covers | Effect on cost |
|---|---|---|
| Model strategy | Hosted API, open-source model, adapters, or fine-tuning on brand data | Licensing, engineering, and infrastructure |
| Data readiness | Cleaning, structuring, and access to source data | Prep time before build; messy data is the most common cause of delays |
| Output type and volume | Text, image, audio, or video, and how many outputs you need | Compute, inference, quality checks, and storage |
| Enterprise integrations | ERP, CRM, CMS, DAM, identity, and APIs | Build time and ongoing maintenance |
| Security and governance | Permissions, audit logs, and rights tracking | Architecture and review effort |
| Ongoing operations | Monitoring, model updates, and support | Recurring costs after launch |
Model
Fixed-scope project
How it works
A defined deliverable with an agreed budget
Suits Clear requirements
Model
Time and materials
How it works
Flexible scope that changes as you learn
Suits Evolving projects
Model
Dedicated team
How it works
Hire generative AI developers who join your team for ongoing work
Suits Long-term builds
Explore practical guides on model choice, RAG evaluation, and agent design to help plan and assess your next generative AI project.
How to choose between self-hosted models and cloud LLM APIs for enterprise chatbots.
Read article
How to test a RAG system for retrieval quality, faithful answers, citations, and human review.
Read article
How goal-driven agents plan, use tools, and act with oversight, plus the patterns and risks that come with them.
Read articleExplore client feedback on AI delivery, automation, and integration. Discuss how this experience can support your generative AI project.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, RoofAid USA
Reyzal Razmi
All Star Influencers
Their product had high accuracy rate of 90% due to which my customers were very happy.
Talk to our generative AI team about your use case, data, and integration needs. We will help scope your LLM application, RAG assistant, or AI agent.
Request a Free ConsultationGenerative AI development services cover the design, build, integration, and support of applications that create text, images, audio, video, or code. A typical project includes use-case selection, architecture, model setup, data connections, testing, and deployment. You receive a working application tied to your data and systems, along with documentation and a plan for upkeep. Some teams need only one stage, such as a prototype or an integration.
Cost depends on model strategy, data readiness, integrations, security needs, and ongoing operations. Typical market ranges start at about $15,000 for a proof of concept. A custom generative AI application often runs $80,000 to $500,000, while enterprise platforms with several integrations start near $300,000. After a short scoping call, we give you an estimate based on your actual requirements.
Timelines depend on data readiness, the number of integrations, and testing needs. A prototype on one workflow often takes a few weeks. A production release takes longer, because it adds integrations, safeguards, and monitoring. Delays usually come from messy source data or slow approvals, not from the model. We agree on milestones after discovery, so you know what arrives at each stage.
Yes. We use your approved content, style guides, and reference material to keep output on brand. Often, retrieval and well-built prompts are enough. When they are not, we test adapters or fine-tuning on a prepared dataset. We only recommend fine-tuning when evaluation shows a gap. Your data stays under the access and storage rules you set.
We set acceptance criteria before the build, such as accuracy, brand fit, format, and required elements. Automated checks then score each output, and reviewers handle edge cases and anything flagged. We also track the share of outputs approved, so drift shows up early. For content pipelines, we watch cost per accepted asset: generation plus review cost, divided by approved outputs. As a result, a cheaper model that produces more rejects cannot hide its real cost.
We start by agreeing on which sources and models you may use. We then record where each output came from, including the model, prompt, reference assets, and reviewer. Rights data stays attached to each asset. Consent and licensing checks run before generation, not after. Legal responsibility stays with your counsel, so we design the system to give them a clear record.
Only if you allow it. Before any build, we agree on which model providers may process your data, what is stored, and for how long. Where data cannot leave your environment, we run open models such as Llama in your own cloud. Prompts and logs then stay under the retention rules you set.
Yes. We start with an audit of your current pipeline, covering output quality, rejection rates, speed, and cost per approved asset. We then rank fixes by impact. Common changes include better prompts, stronger retrieval, caching, model routing, and added review checks. You get a clear list of improvements, and we apply them in stages so your live system keeps running.
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