SoftBrixAI
Generative AI Services

Production-Grade Generative AI Development Services

We replace fragile demonstration chatbot boxes with resilient, enterprise-grade cognitive engines. Our engineering teams configure database-grounded search indices, build multi-agent state graph pipelines, and deploy sovereign open-weight models within secure private virtual clouds.

Written by Umar Abbas (Principal AI Architect)
verified Reviewed by Amir Iqbal (Senior AI Systems Architect)
Estimate Project Budget Review Maturity Assessment
SYSTEM_STATUS: ACTIVE
Input Query [Prompt] Vector Ground pgvector Claude 3.5 Reasoner Citations Guardrails
TRUSTED BY
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FIX AFIB HEART CARE
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Related Service

Building a customer-facing assistant? Start with our conversational AI development services for RAG, LLM routing, omnichannel deployment and human handoff.

Explore chatbot builds arrow_forward
Diagnostic Assessment

Hardening Fragile Prototypes for Enterprise Execution

Building a proof of concept requires days using commercial APIs. Shipping production-grade systems requires resolving security boundaries, verification evaluations, and token cost variables. We audit and transform simple chatbots into reliable pipeline steps.

System Maturity Benchmark

Analyze technical differences between demonstration pilots and hardened pipelines.

Metric Dimension Demo Pilot (Fragile) Hardened Production (SoftBrix)
Data Grounding Naive RAG (simple text split, no re-ranker, direct LLM feed) Hybrid retriever + cross-encoder re-ranking + citation check
Hallucination Gate None (relies on prompts like "be factually accurate") Llama Guard + programmatic citations validator checking source links
Loop Boundary Infinite recursion risks (agents looping calls indefinitely) State-graph checkpoints + deterministic recursion limiters
Evals & Testing Eyeball validation (manual prompt testing on 5 samples) Static golden test dataset + CI/CD automated regression scoring
PII / Security Direct commercial API feed (sends customer data out) Gateway-level Presidio redaction + private VPC weight hosting
Hosting & Cost Unrestricted API calls (unpredictable token spikes) Dynamic LLM routing + quantizations + local inference engines
Architecture Blueprint

Interactive System Delivery Diagrams

Click individual steps in our delivery blueprints to inspect their recommended software choices, telemetry systems, and built-in safety controls.

System Architecture Blueprints

Toggle modes to inspect structural nodes, stack choices, and safety boundaries.

Pipeline Flow (Tap to inspect)
Node Inspector
Select any pipeline node above

Click any circle or process card in the diagram to inspect its operational purpose, recommended tools, and failure mitigation controls.

Tracing Tracks prompt trees and tool trace spans
Evals Automated test suite validates accuracy metrics
Cost Telemetry Monitors request budget caps
Drift Detection Flags performance deviations
Audit Log Immutable record of agent outputs
Compliance Class Determines EU AI Act risk categorizations
Modular Services

Production Capabilities Explorer

We design and ship clean, decoupled capabilities following Model Context Protocol standards. Select options to inspect technical structures.

Custom RAG Ingestion

Grounding DBs

Ingests multi-format corporate records (PDFs, spreadsheets, slides) while preserving visual layout context. Structures documents into semantic passages before vector embedding indexing.

Recommended Tech Stack
LlamaParse Unstructured text-embedding-3-large pgvector
Configuration Specification 
{
  "chunking": "layout-aware",
  "overlap_tokens": 128,
  "vector_store": "pgvector",
  "index_type": "HNSW",
  "dimensions": 1536
}
Production-ready build block Explore Capability arrow_outward
Framework Selector

Alignment and Recommendation Helper

Map your primary operational requirement to receive recommended structural patterns, costing parameters, and developmental blueprints.

Architecture Selector

Select your primary system requirement to determine the recommended technical blueprint.

Scoping & Pricing

Indicative Project Cost Estimator

Configure your development scope, database readiness, and integration counts to compute estimated ranges and timeline percentages.

Scope Parameters

2 APIs

Estimated Scoping Output

Indicative Cost Range $45,000 – $75,000
Target Duration 6 – 10 Weeks
Budget Allocations
Discovery & Scoping 15%
Data Engineering & Chunking 15%
Model Tuning & Retriever Build 30%
System Integration & MCP Servers 20%
Regression Evals & Guardrails 10%
Inference & LLMOps Setup 10%
Note: Ranges represent structural estimates. Request Hard Scoping Audit

Structural Scoping Reference Bands

Proof of Concept (POC)
$18,000 – $32,000 3 – 5 Weeks
  • • Scoping document & use-case score card
  • • Functional system using managed APIs
  • • Basic vector search ingestion sandbox
  • • Evaluation report on initial test set
Request Proof Scope →
Production Feature integration
$45,000 – $85,000 6 – 10 Weeks
  • • Hybrid retriever with re-ranking logic
  • • Docker/VPC deployment specifications
  • • Model Context Protocol adapters (up to 3 APIs)
  • • Deterministic loop boundaries (LangGraph)
  • • PII filter & automated regression eval suite
Request Production Scope →
Enterprise AI Platform
$120,000 – $240,000+ 12 – 20 Weeks
  • • Multi-agent graph with dynamic tool routers
  • • Private cloud / Air-gapped on-premise install
  • • Fine-tuned adapters (LoRA/PEFT) on GPU pods
  • • Model distillation pipelines & inference caching
  • • Full compliance logging under EU AI Act rules
Request Enterprise Scope →
Risk & Governance

EU AI Act Compliance Risk Classifier

Identify the regulatory risk classification of your proposed domain to review compliance logging obligations and governance protocols.

EU AI Act Classifier

Classify your generative AI application domain to evaluate regulatory obligations.

Application Class Limited Risk

Customer Support Chatbot

Automates customer ticketing, question resolution, and basic product documentation responses.

Compliance Obligations Checklist
  • check_box Explicitly disclose to users that they are interacting with an AI system.
  • check_box Establish basic audit logs monitoring conversation inputs.
  • check_box Ensure standard security and GDPR privacy rules are followed.
Target compliance baseline Auditing Details arrow_outward
Model Comparison

Frontier Models & Tech Stack Grid

Filter model versions based on reasoning capability, cost parameters, and open weights to establish host configurations.

Frontier Model Matrix

Filter model capabilities to find the right logical and hosting fit for your pipeline.

Model Provider Reasoning Open Weights Low Cost MCP Ready Target Pipeline Use Case
Claude 3.5 Sonnet 2024-06-20 Anthropic ✔ — — ✔ Primary agent planner and complex structural reasoning node.
Claude 3.5 Haiku 2024-11-04 Anthropic — — ✔ ✔ Fast utility orchestration, syntax checking, and router nodes.
GPT-4o 2024-05-13 OpenAI ✔ — — ✔ Cross-functional extraction and high-volume vision inputs.
GPT-4o-mini 2024-07-18 OpenAI — — ✔ ✔ High-throughput semantic tagging, basic validation, and text routing.
o1-mini 2024-09-12 OpenAI ✔ — — — Complex mathematical operations, code generation, and recursive verification.
Llama 3.3 70B 2024-12-06 Meta ✔ ✔ ✔ ✔ Air-gapped enterprise pipelines requiring local weights sovereignty.
Llama 3.1 8B 2024-07-23 Meta — ✔ ✔ ✔ Ultra-low latency local processing or edge adapter executions.
Mixtral 8x22B 2024-04-10 Mistral AI ✔ ✔ ✔ ✔ High-concurrency private agent loops and batch inference tasks.
Shipping Process

The 4-Phase Delivery Timeline

How we move projects from initial database scoping assessments to validated and audited production deployments.

Phase 01 1 - 2 Weeks
SoftBrix AI Delivery

Scoping, Architecture & Feasibility

We map raw data models, assess chunking requirements, and design system topology. We establish the Golden test set.

Phase Deliverables
  • • Technical design blueprint & topology mapping
  • • Initial golden test dataset (50+ annotated query-response pairs)
  • • Feasibility and compute footprint projection report
Phase 02 2 - 3 Weeks
SoftBrix AI Delivery

Chunking, Ingestion & Vector Indexing

We construct layout-aware ingest pipelines and configure vector indexes. We optimize hybrid search formulas.

Phase Deliverables
  • • Dockerized document ingestion parser pipelines
  • • Configured hybrid database indexes (pgvector or Qdrant)
  • • Optimized Reciprocal Rank Fusion retrieval parameters
Phase 03 3 - 5 Weeks
SoftBrix AI Delivery

Orchestration, Tooling & Prompts

We build state-graph agent structures and Model Context Protocol servers. We tune adapter layers and prompt scripts.

Phase Deliverables
  • • LangGraph multi-agent orchestration codebases
  • • Secure MCP servers linking models to system APIs
  • • Fine-tuned model adapters (LoRA) hosted on vLLM clusters
Phase 04 1 - 2 Weeks
SoftBrix AI Delivery

Automated Evaluation, Safety & Launch

We connect automated regression tests and safety guardrails. We set up Telemetry tracing hooks and publish logs.

Phase Deliverables
  • • Automated evaluation suites (Promptfoo assertion gates)
  • • Presidio PII redaction & Llama Guard input safety gates
  • • Opentelemetry telemetry dashboards mapping token expenses
Use Cases

Production Use Cases by Industry Segment

Explore how we build secure systems complying with privacy guidelines across specific operational verticals.

Industry Use Case 99.8% Grounding Rate

Legal Services

Analyzes contract agreements semantically, extracts high-risk clauses, and validates references against official precedent registers.

Production Stack
pgvector Cross-Encoder Re-ranker o1-mini
Secure, localized architectures Explore Industry Solutions arrow_outward
Why SoftBrix

Rigorous Engineering Methodologies

We apply regression testing rules, open protocol standards, and retry depth checkpoints to keep developments predictable.

Regression Prevention

The Golden Set Discipline

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We eliminate eyeballing. Every prompt update, model switch, and RAG configuration changes are automatically tested against a static, human-vetted validation dataset.

Failure Mitigation

Prevents silent model regression where improving one response breaks ten other existing query loops.

SoftBrix AI Production Engineering Standards
Promptfoo Evaluation Script Output 
vars:
  query: "What are the compliance controls for legal data?"
tests:
  - assert:
      - type: contains-all
        value: ["SOC 2 Type II", "PII redaction"]
      - type: latency
        threshold: 3000
Vendor Agnosticism

Decoupled Logic (MCP)

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We construct all tool integrations using the open Model Context Protocol (MCP) standard. LLM cognitive loops are completely decoupled from external tool API executions.

Budget Control

Deterministic Loop Boundaries

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We build agents using state-graph controls (LangGraph). Every execution node features absolute threshold rules limiting loops, retry cycles, and concurrent execution budgets.

FAQs

Frequently Asked Questions

Filter or search our reference index to address common queries surrounding models, timelines, and compliance rules.

search
What are generative AI development services? expand_more

Generative AI development services build systems that generate text, code, or media. We engineer custom architectures using grounding databases and agentic control layers. These systems execute actions rather than serving as basic chat bubbles. We deliver production-grade configurations built for predictable performance.

What is the difference between generative AI and agentic AI? expand_more

Generative AI produces content based on immediate prompts. Agentic AI uses reasoning loops to decompose complex goals. Agents execute tools, review their own output, and adapt paths dynamically. This autonomy resolves multi-step operations without requiring human prompts for each step.

What is RAG, and why do enterprise GenAI systems need it? expand_more

Retrieval-Augmented Generation (RAG) queries external databases to extract matching records before calling a model. This context grounds model responses in factual corporate data. RAG eliminates silent hallucinations. It updates system answers instantly without requiring expensive model retraining.

Should I use RAG or fine-tuning? expand_more

Use RAG when your target data updates frequently or needs precise source citation. Use fine-tuning when the model must adapt its tone, format, or specialized reasoning patterns. Most enterprise systems deploy both architectures in tandem for optimal accuracy and speed.

How much does generative AI development cost? expand_more

Production features cost between $35,000 and $85,000. Full-scale platform integrations range from $120,000 to $240,000. Proof-of-concept builds cost between $15,000 and $25,000. We define project budgets transparently using structural complexity metrics rather than vague estimates.

How long does it take to build a production generative AI system? expand_more

A production feature integration takes 6 to 10 weeks to ship. Enterprise platforms require 12 to 20 weeks of engineering. We deliver a functional proof of concept in 3 to 5 weeks to validate technical viability before starting production builds.

How do you prevent hallucinations? expand_more

We ground model responses using hybrid database retrievers. We verify citations against source passages before displaying answers. Our execution pipelines apply structured validation layers that reject malformed outputs. This architecture forces models to abstain when confidence metrics fall below thresholds.

Can generative AI run on-premise or in a private cloud? expand_more

Yes, we deploy open-weight models inside private virtual clouds or on-premise hardware. We host models like Llama 3.3 using vLLM containers. This ensures your corporate data remains within your security perimeter. No sensitive information leaves your private network.

How do you evaluate whether a GenAI system is actually working? expand_more

We evaluate models against a static golden set of reference cases. We run regression suites evaluating semantic matching, citation accuracy, and response structure. Our monitoring pipelines track trace logs to pinpoint exactly where tool failures occur.

What is MCP (Model Context Protocol) and why does it matter? expand_more

Model Context Protocol (MCP) is an open standard connecting models to data sources and tools. It establishes uniform schemas for API communication. MCP prevents vendor lock-in. It allows models to call internal databases and developer tools using secure connections.

Can generative AI integrate with our existing ERP / CRM / internal systems? expand_more

Yes, we bridge models to platforms like Salesforce, HubSpot, or Jira. We construct secure API adapters using typed schemas. The system validates model payloads before transmission. This ensures tool operations conform to API constraints and access boundaries.

How do you handle EU AI Act and GDPR compliance? expand_more

We categorize applications under risk classes first. High-risk systems receive conformity documentation and logging capabilities. We build Presidio redaction layers to scrub PII from model inputs. This design aligns pipelines with EU risk obligations and privacy regulations.

What does an LLMOps pipeline actually include? expand_more

Our LLMOps pipelines establish automated regression gates inside CI runs. We configure OpenTelemetry adapters to track prompt costs and execution latency. The system records trace trees for audit reviews. It flags performance deviations when models degrade in production.

How do you choose the right model for our use case? expand_more

We benchmark tasks against model capabilities and hosting economics. We route basic extraction steps to fast, cheap models like GPT-4o-mini. We activate advanced reasoning models like Claude 3.5 Sonnet only when solving complex logic. This routing minimizes overall token costs.

Glossary

Technical Terms Index

Technical Entity Glossary

Vetted definitions of key concepts and architectural components in LLM engineering.

RAG
Retrieval-Augmented Generation. Grounds model responses by retrieving relevant passages from corporate databases before generating output.
Hybrid retrieval
Combines vector similarity searches with sparse keyword matches to capture both conceptual intent and exact terms.
Re-ranking
Applies secondary deep learning models to rank retrieved documents, ensuring the most relevant passages sit at the top of prompt context.
Embeddings
High-dimensional vectors representing the semantic meaning of text chunks, allowing mathematical comparison of conceptual similarity.
Vector database
Specialized storage optimized to index, store, and query high-dimensional embeddings with sub-millisecond latencies.
LoRA/PEFT
Parameter-Efficient Fine-Tuning modifying small adapter layers while keeping base weights frozen, minimizing training compute costs.
RLHF
Reinforcement Learning from Human Feedback. Aligns model outputs with human preferences through reward predictors.
Distillation
Transfers capabilities from a large frontier model to a smaller, faster model, cutting hosting fees and latency.
Quantization
Reduces model weight precision (e.g., from 16-bit to 4-bit), cutting memory footprints to run models on cheaper hardware.
Guardrails
Software safety layers that filter inputs and outputs, blocking private data leaks, offensive text, or structural deviations.
Hallucination
Model outputs containing incorrect or ungrounded assertions presented as factual statements.
Golden set
Vetted collection of prompt-response pairs representing target performance standards used for automated regression tests.
Eval harness
Testing framework executing models against golden sets to score accuracy, citation rates, and formatting compliance.
Drift
Gradual performance decline of a model over time as production data diverges from original training parameters.
MCP
Model Context Protocol. An open-standard API letting models interact securely with databases, IDE tools, and files.
Agentic orchestration
Coordinates specialized sub-agents via state graphs, managing multi-step workflows without static coding rules.
HITL
Human-in-the-Loop. Safety design pausing autonomous agent actions for human review before executing high-risk transactions.
LLMOps
Engineering practices governing continuous evaluation, version control, hosting, and tracing of language models in production.
Inference server
Specialized runtime host optimizing GPU memory usage and request concurrency to deliver fast model responses.
Context window
Maximum amount of token data a model can read and process in a single execution call.
Token cost
Financial fees charged per raw chunk of text processed or generated by a hosted model.

Ready to build production-grade AI?

Schedule a scoping meeting with our systems architects to map model feasibility and compute footprints.

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