LLM Application Development
Custom copilots, chat assistants and content tools built on GPT, Claude, Gemini or open-weight models, integrated into your web, mobile and internal platforms with secure authentication.
Generative AI Development
Generative AI Products Built for Real Business Outcomes
We design, build and operate LLM applications, retrieval-augmented assistants and autonomous agents that plug into your data and workflows, with the evaluation, guardrails and cost controls needed to run them safely in production.
6wks
Typical time to production pilot
40%
Target reduction in manual effort
95%
Answer-accuracy target on eval sets
Generative AILLM apps & AI automation
Tech we use
24+ tools
OpenAIAnthropicGoogle GeminiMistral AI
Meta
Hugging Face
Ollama
LangChain
LangGraph
CrewAI
Python
FastAPI
PyTorch
Qdrant
PostgreSQL
Elasticsearch
Redis
MongoDB
n8n
Zapier
AWS
Microsoft Azure
Google Cloud
DockerGenerative AI
From AI demo to dependable product
Most generative AI initiatives stall between an impressive prototype and a system the business can actually trust. RixlSoft closes that gap. We start from a measurable use case, ground models in your own documents and systems through retrieval, and wrap every feature in evaluation suites, access controls and observability so quality is proven before it reaches users.
As an AI-first engineering company, we work across commercial and open-weight models, choosing the right one per task on accuracy, latency and cost. You get a maintainable codebase, clear prompts and pipelines, and a team that stays to tune performance after launch.
- Model-agnostic, vendor-neutral architecture
- RAG grounded in your private data
- Evaluation and guardrails from day one
- Token cost monitoring and optimization
6wksTypical time to production pilot
What We Offer
Generative AI Services
Comprehensive capabilities covering every stage of your Generative AI journey — delivered by senior specialists.
Retrieval-Augmented Generation
Knowledge assistants that answer from your documents, wikis and databases with citations, using tuned chunking, hybrid search, re-ranking and vector stores such as Qdrant or Postgres.
AI Agents and Multi-Agent Systems
Tool-using agents that research, draft, reconcile and act across your systems, orchestrated with LangGraph or CrewAI and bounded by approvals, audit logs and clear escalation paths.
AI Workflow Automation
Intelligent automation that classifies emails, extracts data from documents, routes tickets and updates CRMs, combining LLMs with n8n, Zapier or custom pipelines to remove repetitive work.
Fine-Tuning and Model Customization
Domain adaptation of open-weight models with curated datasets, LoRA fine-tuning and distillation, delivering smaller, faster models where prompting alone cannot meet quality or cost targets.
LLMOps, Evaluation and Guardrails
Automated evaluation suites, prompt versioning, tracing, PII redaction and jailbreak defenses, so every model or prompt change is measured against real test cases before release.
Why RixlSoft
Why Choose Us for Generative AI
Senior engineers, transparent delivery and AI-first thinking — the difference between shipping software and building an asset.
Use-Case First
We prioritize use cases by measurable value and feasibility, so budget goes to features that save hours or raise revenue rather than to experiments that never ship.
Model-Neutral Choices
We benchmark providers on your own data and design abstraction layers, letting you switch models as pricing and capability change without rewriting your application.
Security and Privacy Built In
Private deployments, role-based retrieval, data residency options and redaction keep sensitive information inside the boundaries your compliance and security teams expect.
Predictable Running Costs
Caching, routing, prompt compression and right-sized models keep per-request costs low, with dashboards that show token spend per feature, team and customer.
Our Process
How We Deliver Generative AI
A proven, transparent process with clear deliverables at every stage — so you always know what's done, what's next and why.
01
Discovery and Use-Case Mapping
We audit workflows, data sources and constraints, then rank candidate use cases by value, risk and feasibility to pick a focused first release.
Use-case matrixData auditSuccess metrics
02
Proof of Value
A working prototype on your real data, measured against a golden evaluation set, confirms achievable accuracy, latency and cost before larger investment.
PrototypeEvaluation setFeasibility report
03
Architecture and Data Pipelines
We design ingestion, embeddings, retrieval, model routing and security layers, choosing infrastructure that fits your existing cloud, data residency and compliance requirements.
Solution architectureIngestion pipelineSecurity model
04
Build, Evaluate and Harden
Features are built in short sprints with automated evaluations, guardrails, observability and human-in-the-loop controls tested against edge cases and adversarial prompts.
Production buildEval dashboardGuardrail suite
05
Launch and Continuous Tuning
We roll out gradually, monitor quality and spend, collect user feedback, and iterate on prompts, retrieval and models as usage grows.
Rollout planMonitoringMonthly tuning report
Technology Stack
Tools & Platforms We Master
Proven, production-grade technology chosen for your requirements — never for hype.
OpenAIAnthropicGoogle GeminiMistral AIMetaHugging FaceOllamaLangChainLangGraphCrewAIPythonFastAPIPyTorchQdrantPostgreSQLElasticsearchRedisMongoDBn8nZapierAWSMicrosoft AzureGoogle CloudDockerEngagement Models
Flexible Ways to Work Together
Choose the model that matches your scope, budget and pace — and switch as your needs evolve.
AI Proof of Value
A fixed-scope four to six week engagement that validates one use case on your data with clear accuracy and cost metrics.
Discuss this modelDedicated AI Squad
A cross-functional team of AI engineers, backend developers and QA embedded in your roadmap for ongoing product development.
Discuss this modelTime & Material
Flexible hourly or monthly billing for evolving requirements, integrations and iterative model tuning where scope is still emerging.
Discuss this modelIndustries
Industries We Serve
Banking & Fintech
Payments, lending & financial platforms
Healthcare & Pharma
Digital health & clinical platforms
Retail & E-Commerce
Commerce platforms & automation
Education Technology
LMS, AI tutors & learning tools
Logistics & Ops
Supply chain & route optimization
Enterprises
Modernization at scale
FAQs
Frequently Asked Questions
Everything you need to know about our Generative AI services. Can't find an answer? Talk to our team.
Ask an ExpertHow much does a generative AI project cost?
Cost depends on the number of integrations, data preparation effort, security requirements and whether fine-tuning is needed. A focused proof of value is typically the smallest engagement, while production systems with agents and multiple data sources take longer. We provide a fixed estimate after a short discovery, along with projected monthly model and hosting costs.
How long does it take to launch an AI assistant or agent?
A proof of value on real data usually takes four to six weeks. A hardened production release with authentication, monitoring, evaluations and integrations typically follows within another six to ten weeks. Complex multi-agent workflows or heavy data cleanup can extend timelines, which we flag early during discovery.
Will our data be used to train public models?
No. We use enterprise API tiers that exclude your data from training, or deploy open-weight models in your own cloud when stricter isolation is required. Retrieval respects your existing permissions, and we can add PII redaction, encryption and audit logging to meet your internal security and regulatory policies.
Who owns the code, prompts and fine-tuned models?
You do. All source code, prompts, evaluation datasets, pipelines and fine-tuned model weights produced under the engagement are transferred to you. We document architecture and operating procedures so your team, or another vendor, can maintain and extend the system without depending on RixlSoft.
How do you handle hallucinations and quality after launch?
We ground answers in retrieved sources with citations, set confidence thresholds, and route uncertain cases to humans. After launch, automated evaluations run on every change, production traces are sampled for review, and our support plans include regular tuning of prompts, retrieval and model selection as usage patterns evolve.
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Learn moreReady to start your Generative AI project?
Book a free discovery call. We'll map your goals, suggest the right approach and give you a clear estimate — no obligation.
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Let's Build Something Extraordinary
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