Custom LLM Fine-Tuning
Fine-tune GPT, Llama, Mistral and Claude on your proprietary data with LoRA and QLoRA. Domain-specific behaviour at a fraction of the cost of training from scratch — and without sending your data to a public model.
Generative AI Development Services
Production-grade generative AI that creates text, code, images and documents from your data — with custom fine-tuning, RAG, enterprise guardrails and reliable output quality at scale. Not another wrapper around ChatGPT.
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50+
AI & ML Specialists
100+
Projects Delivered Since 2014
$5K
Working POC in 5 Days via APEX
4.9
Clutch Rating (34 Reviews)
Our Generative AI Capabilities
Nine capability areas covering text, code, image and document generation — backed by retrieval, fine-tuning and guardrails you can actually take to production.
RAG System Development
Connect LLMs to your knowledge bases through vector search, hybrid retrieval and re-ranking. Answers grounded in your actual documents — with source citations and confidence scoring.
AI-Powered Content Generation
Generate reports, policy documents, marketing copy, customer correspondence and regulatory filings at production quality. Consistent voice, required disclosures, brand-safe outputs.
Intelligent Document Processing
Extract structured data from PDFs, scanned forms, contracts and invoices. OCR + LLM + validation — turns 30-minute manual extraction into a 30-second pipeline.
AI Copilots for Enterprise
Copilots embedded in the tools your teams already use — Salesforce, ServiceNow, Notion, internal portals — for drafting, summarisation and decision support without context-switching.
Generative AI for Images
Product visualisation, marketing creative and synthetic training data using Stable Diffusion, DALL-E and ControlNet. Style-locked outputs that match your brand.
Code Generation & Dev Tools
Automated code review, test generation, documentation writing and migration assistants tuned to your stack — not generic Copilot suggestions.
Multi-Modal AI Solutions
Systems that read and produce across text, images, audio and structured data — useful when one input alone (just text, just an image) does not capture the task.
Responsible AI & Guardrails
Output filtering, bias detection, prompt-injection protection and PII redaction. Audit trails and human-in-the-loop checkpoints baked in from day one.
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Why Production-Grade Generative AI Is Hard
Five challenges that separate impressive demos from systems that survive contact with real users, real data and real auditors.
Hallucination Is the Default
LLMs generate plausible but potentially incorrect text — confidently. We solve this with RAG grounding, citation verification, confidence scoring and automated evaluation harnesses, not by hoping users notice the mistake.
Cost Spirals Are Real
A demo costs $200/month. The same demo at production scale can hit $20K/month. Model routing, caching, quantisation and selective fine-tuning typically cut the bill by 40–60% without quality loss.
Data Privacy Is Non-Negotiable
Most enterprise data cannot leave your perimeter. We deploy on-premise, in your VPC or on Bedrock/Azure OpenAI with data residency controls, PII redaction and per-tenant isolation.
Latency Kills Adoption
If a user waits 15 seconds, they leave. Quantisation, streaming responses, speculative decoding and edge caching push interactive workflows under one second.
Compliance Is a Moving Target
EU AI Act, HIPAA, SOC 2, FINRA — requirements change quarterly. Audit trails, model cards, human-oversight checkpoints and risk classification are built in from day one, not bolted on at launch.
Industry Applications
Route Documentation
Shipment paperwork generated automatically from TMS, ERP and carrier-portal data — no manual re-typing across systems.
Carrier RFP Responses
Draft tailored RFP responses and exception reports in minutes by combining historical wins, lane data and current capacity.
Demand Forecasting Narratives
Turn forecast model output into human-readable weekly reports that ops, finance and customers can actually consume.
Customs Declarations
Automate bills of lading, customs declarations and commercial invoices from order data — with country-specific compliance.
Our Generative AI Development Process
- 1
Use Case Discovery & Feasibility
Audit your workflows and data assets. Identify the highest-impact-to-complexity use cases and reject the ones LLMs are not actually good at — saves months of wasted effort.
- 2
Data Strategy & Model Selection
Evaluate models against accuracy, latency, cost and compliance constraints. Route simple tasks to cheaper models, reserve frontier models for the hard 10%.
- 3
Development & Fine-Tuning
Build retrieval pipelines, implement prompt and tool-use frameworks, fine-tune where it earns its keep. Bi-weekly demos against real data, not slideware.
- 4
Guardrails, Testing & Compliance
Quality evaluation harness, hallucination detection, bias auditing, prompt-injection tests and compliance verification — every change goes through the same gate.
- 5
Deployment & Continuous Improvement
Production monitoring for quality, latency, cost and user satisfaction. Expect a 15–25% quality lift in the first 90 days once real-user feedback feeds the eval set.
Technology Stack
- LLMs
GPT
Claude
Llama
Mistral
Gemini
- Fine-Tuning
- LoRA
- QLoRA
- RLHF
- DPO
- RAG Frameworks
LangChain- LlamaIndex
Haystack
- Vector Databases
Pinecone- Weaviate
Qdrant
pgvector
Chroma
- Guardrails
NeMo Guardrails- custom validation
- prompt-injection protection
- Cloud AI
AWS Bedrock
Azure OpenAI
Vertex AI
- MLOps
MLflow
Weights & Biases- A/B testing
- eval harnesses
- Infrastructure
Docker
Kubernetes
Terraform- GPU orchestration
Why Choose Softermii for Generative AI
| Criteria | Softermii | Big Consultancies | AI Startups |
|---|---|---|---|
| Production Experience | 100+ AI projects delivered since 2014 | Strong on strategy, light on shipping | First few projects often in flight |
| Proprietary Technology | APEX agentic platform + reusable RAG building blocks | Vendor partnerships, no internal IP | One product, narrow domain |
| Industry Knowledge | Deep in fintech, healthcare, insurance, logistics | Broad but generic | Usually a single vertical |
| Cost Transparency | Fixed-scope proposals, public tier pricing | Time-and-materials with change orders | Per-seat SaaS plus services |
| Ongoing Support | Dedicated team continuity post-launch | Hand-off to a separate run-team | Roadmap dictated by their product |
| Certifications | HIPAA, SOC 2, GDPR, PCI DSS aware delivery | Yes — at a consultancy price | Limited; not always audit-ready |
Generative AI Development Cost
POC / Prototype
$5K – $15K
- Working demo on your data
- Feasibility validation
- Model & architecture recommendation
- ROI projection
Time: 1 – 3 weeks
Start POCSingle Use-Case MVP
$15K – $50K
- Production-ready system
- One workflow automated end-to-end
- System integration & SSO
- Guardrails & monitoring
Time: 4 – 8 weeks
Get StartedEnterprise GenAI Platform
$50K – $250K+
- Multi-use-case platform
- Enterprise integrations & SSO
- Compliance, audit, governance
- Fine-tuned models + RAG infra
Time: 3 – 8 months
Get StartedWhat Affects Cost
- Number of integrations and source systems
- Data preparation and labelling complexity
- Compliance requirements (HIPAA, SOC 2, EU AI Act)
- Fine-tuning vs RAG-only architecture
- On-premise / VPC deployment vs managed cloud
Why this stays affordable
- Clean data cuts development time by 30–50%
- APEX building blocks remove 40–60% of bespoke infra work
- Model routing + caching typically cuts run-cost by 40–60% in year one
Most generative AI projects fail not because the models aren't good enough — they fail because teams treat prompting as engineering. Real generative AI development means building retrieval systems that surface the right context, evaluation frameworks that catch failures before users do, and deployment infrastructure that keeps costs from eating your margin. The model is 20% of the work. The system around it is the other 80%.
CEO & Co-Founder, Softermii
Slava Vaniukov
Frequently Asked Questions
What are generative AI development services?
Generative AI development services cover the design, build and deployment of systems that generate content — text, code, images, structured documents — using large language models and diffusion models. The work covers fine-tuning, RAG, guardrails, evaluation, integration and operations, not just prompt engineering.
How much does generative AI development cost?
POC / prototype: $5K–$15K (1–3 weeks). Single-use-case MVP: $15K–$50K (4–8 weeks). Enterprise platform: $50K–$250K+ (3–8 months). With APEX you can start with a working proof of concept for $5K in 5 days and only commit to the full build once feasibility is proven.
How long does it take to build a generative AI solution?
Using APEX, a working proof of concept ships in 5 days. A single-use-case MVP takes 4–8 weeks. Enterprise platforms run 3–8 months with bi-weekly demos against real data. Timelines are typically 40–60% shorter than building everything from scratch because the retrieval, eval and guardrail layers come pre-built.
Can generative AI work with our existing data and systems?
Yes. RAG architectures connect LLMs to databases, CRMs, ERPs, data warehouses, SharePoint and file shares. We integrate with Salesforce, HubSpot, Zendesk, ServiceNow, Jira and most major systems, plus custom APIs for legacy stacks. On-premise and VPC deployments keep data within your perimeter.
How do you prevent AI hallucination in production?
Layered defence: RAG grounding so answers reference your real documents, citation verification, confidence scoring, automated eval harnesses, and human-in-the-loop checkpoints for high-stakes outputs. We treat hallucination as a measurable engineering metric, not a vibes-based concern.
Is generative AI compliant with GDPR and the EU AI Act?
Yes. We support data residency controls, PII redaction, risk classification per the EU AI Act risk tiers, model cards, audit trails and on-premise deployment. HIPAA, SOC 2 and PCI DSS aware delivery is standard, with the right cloud vendor and contractual setup.
What is the difference between fine-tuning and RAG?
Fine-tuning permanently changes the model so it speaks in your style and knows your domain at training time — useful for tone, format and behaviour. RAG retrieves relevant information at query time and lets the model cite it — useful when the underlying facts change often. Most production systems combine both: fine-tune for behaviour, RAG for facts.
Do we own the AI model and code you build?
Yes, completely. You own the custom code, prompts, fine-tuned weights, RAG configurations and infrastructure-as-code. We do not lock you into a proprietary platform. If you want to bring development in-house later or switch vendors, you can — no data hostage situations.
Ready to Build Production-Grade Generative AI?
Tell us about your use case and we will deliver a detailed proposal with architecture recommendations, timeline and a fixed-price estimate — within 5 business days.
Don't Dream for Success, Let Us Make It Real
Tell us what you're building. We'll tell you how fast we can ship it — and what it'll cost.
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