Agentic AI
We apply Agentic AI in customer data applications to drive action.
Tailored Model Design
Running a GPT is easy. Using it inside a multi-step agentic process to solve your specific business problems is where most teams hit a wall — and where we come in. In the past two years we've been designing bespoke models that deliver impactful results with vastly greater computing efficiency.
We believe we are at a genuine inflection point. The companies that rewire how they work — not just the ones that generate the power — are the ones that define the next decade. Every model we build at BMG starts with your data, your goals, and your operational reality. The output is never a generic tool. It's a purpose-built system aligned to your margins.
Data Without the Noise
By integrating multiple data sources simultaneously — real-time PDFs from stock exchanges, research papers, inventory systems, and live customer data — we deliver actionable insights in seconds. This eliminates the cumbersome task of manually sifting through data sets and enables our agents to correlate relationships and surface decisions instantly.
We are currently collaborating with a select client base to develop bespoke AI models for customer relationship and inventory data. These aren't dashboards — they are autonomous systems that read the data, make a recommendation, and act on it without waiting for a human to log in.
The Power of Agentic AI
Agentic AI goes beyond traditional models by empowering autonomous systems capable of independent decision-making and real-world action. These systems analyze situations, formulate strategies, and execute tasks with minimal human intervention — designed to operate independently, adapt to changing environments, and learn from outcomes.
In essence: Generative AI creates content. Agentic AI drives outcomes. The output of Generative AI is new content; the output of Agentic AI is a series of coordinated actions and decisions. The two work in tandem — creativity combined with execution — to create solutions that are greater than the sum of their parts.
Practical Applications of Agentic AI
In customer data, our agents autonomously analyze behavioral patterns, draw real-time insights, and implement personalized marketing strategies — mapping new cohorts to dynamic profiles and deploying them across channels for maximum impact, without a human touching the workflow.
For inventory management, we plug directly into your ERP or operations platform to monitor stock levels, predict demand, surface loss signals, and alert owners before margin erosion compounds. In healthcare, we recommend content plans based on diagnostic and extrinsic data trends to improve client stickiness. Wherever your business generates data and requires a decision, an agent can own that loop.
We've Been Here Before
When BMG was founded 16 years ago, our first breakthrough was integrating APIs from travel and hotel inventories and weather patterns directly into search engines — driving efficiency gains and significantly increasing clicks and conversions that we then retargeted across performance media for even higher ROAS. Complex at the time. Pretty flat now.
Agentic AI is that same moment again. The teams that rewire their operations around it — not just the ones that read about it — will define the next era of their industries. BMG has been rapidly building new IP to accelerate AI for businesses and we're ready to help you move first.
"The shift is from models that respond to prompts to agents that drive outcomes. Traditional models are systems of language. Agentic systems are systems of behaviour."
Verify Agent
Performs real-time cross-checks against external records and internal systems simultaneously. Once confirmed, the agent autonomously triggers the appropriate downstream workflow — eliminating manual lookup, reducing errors, and converting verification tasks into action without human intervention. Zero latency. Full audit trail.
Precision Agent
Processes complex, multi-variable inputs in real time and returns structured, decision-ready outputs instantly. Removes friction from high-touch calculation workflows by applying live data and business logic together — so your team responds faster, with greater confidence, on every call. No spreadsheets. No delays.
Inventory Agent
Connects directly into your operations platform to continuously track stock performance, movement patterns, and carrying costs. Proactively surfaces prioritised recommendations so decision-makers know exactly when to hold, act, or cut — before margin erosion compounds. Integrates with any ERP that has an API surface.
Growth Co-Pilot
An always-on agentic co-pilot that equips your revenue team with real-time context, intelligent next-best-action recommendations, and automated follow-through — keeping opportunities moving through the pipeline without manual overhead. CRM-native, revenue-intelligent, and built to close the gap between insight and action.
Model Layer
LLMs, reasoning models, and embeddings — the intelligence substrate. We work model-agnostic across OpenAI, Anthropic Claude, and Google Gemini, selecting the right model for the task rather than defaulting to a single provider. This flexibility ensures cost efficiency and best-in-class reasoning for every agent's specific function.
Orchestration Layer — MCP & Semantic Kernel
Model Context Protocol (MCP) — pioneered by Anthropic — is the emerging open standard we use to give agents structured, secure, composable access to external tools and data. Instead of brittle one-off integrations, MCP defines a clean interface between your LLM and the world: databases, REST APIs, CRMs, file systems, and third-party services all become first-class tools the agent can call, read, and write to with OAuth 2.0 permission scoping. Every MCP server we deploy is containerized, versioned, and secured so your data never leaves your control boundary.
On top of MCP, we implement Microsoft Semantic Kernel as the orchestration framework — the connective tissue that links LLMs, tool calls, memory systems, and business logic into coherent multi-step workflows. Semantic Kernel handles prompt templating, function calling, planner-driven task decomposition, and plugin management so agents can reason and act across long-horizon tasks without losing context or entering error loops. Together, MCP and Semantic Kernel are what separate a useful chatbot from a genuinely autonomous agent.
Application Layer — We Build Here
Purpose-built agents that reason against your data, execute against your systems, and adapt against your outcomes. This is where pable.ai operates. Every agent we deploy lives at the application layer — sitting above the model and orchestration infrastructure, wired directly into your workflows, acting on your behalf with full observability and audit logging. No black boxes. No surprises.
Bespoke Model Development
We develop a proprietary model aligned with your specific client goals — not a fine-tuned wrapper on a public model, but an architecture designed from first principles around your data structure, latency requirements, and business outcomes.
Cloud Instance Initiation
We initiate a dedicated instance on Google Cloud or AWS — or configure it within your existing cloud environment. Every deployment is containerized, versioned, and secured with proper IAM scoping, VPC isolation, and secrets management from day one.
API Integration
We build a clean API layer into your current data set for seamless, real-time data flow between your systems and the agent. Whether that's a REST API, GraphQL endpoint, webhook, or direct database connector, we ensure your agent always has access to live, accurate data — never a stale snapshot.
MCP Servers & Semantic Kernel
We build and deploy production Model Context Protocol (MCP) servers that give your agents secure, structured access to every tool and data source they need — your CRM, ERP, databases, and APIs — without brittle custom integrations for each one. Semantic Kernel then sits on top as the orchestration layer, managing tool routing, memory, multi-step planning, and plugin execution so your agents reason coherently and act reliably across complex, long-running workflows.
Fine-Tuned LLMs
We refine large language models into tailored solutions using LoRA and QLoRA adapters on your domain-specific data — improving accuracy, reducing hallucination rates, and cutting inference costs compared to prompting a general-purpose model. The result is an LLM that speaks your business's language fluently.
Continuous Feedback & Adaptation
Every agent we deploy ships with outcome logging, a continuous feedback loop, and adaptive improvement mechanics built in. Agent performance is monitored against KPIs in real time — latency, accuracy, action success rate, and cost-per-outcome — with automated retraining triggers so your agent improves with every cycle.
Data In
1st-party business data, live system APIs, external records, ERP and CRM connections — all flowing into the agent in real time.
Reasoning
Multi-step planning, tool selection, context memory, and self-verification — the agent thinks before it acts, checking its own logic at each step.
Execution
Autonomous action, human escalation gates where needed, system write-back, and real-time alerts and triggers — execution with full accountability.
Learning
Outcome logging, continuous feedback loops, and adaptive improvement — every action the agent takes makes the next one smarter and more precise.