LLM adaptation with control, lineage, and oversight.
Fine-tuning pipelines, evaluation harnesses, and risk-aware adaptation workflows for regulated and mission-critical environments.
What LLM Adaptation Means at Bugni Labs
We do not treat "fine-tuning" as a magic fix.
Adaptation means shaping the model's behaviour through multiple mechanisms:
1. Domain Model Grounding
We embed real domain structures: taxonomies, entity schemas, lifecycle models, event semantics, regulatory categories, risk surfaces.
This makes the model internally consistent and aligned with business truth.
2. Tactical Fine-Tuning (when appropriate)
Not all domains need fine-tuning, but when they do, we focus on: instruction fine-tuning, classification and scoring heads, structured output formats, hybrid datasets combining synthetic + curated data, extremely tight quality assurance and data validation.
We don't fine-tune for style. We fine-tune for precision behaviours.
3. Retrieval and Context Strategy (RAG++ patterns)
We architect retrieval that: preserves domain semantics, separates "source of truth" from hallucination, manages multi-step context retrieval, enriches queries with event/state information, records all retrieval for audit, prevents context poisoning.
RAG isn't a feature—it's an information architecture problem.
4. Tool / API Integration
LLMs often should not "answer"—they should: fetch, calculate, simulate, enrich, classify, escalate, orchestrate agents, or follow procedural steps.
We design tool interfaces and API contracts the model can reliably call.
5. Guardrails & Constraint Mechanisms
Techniques include: dynamic policy enforcement, constrained decoding, grammar-based outputs, decision-boundary tests, safety classifiers and veto layers, hybrid rule-based + model-based reasoning.
The goal: models that can only behave within designed boundaries.
6. Continuous Evaluation & Runtime Observability
LLMs are probabilistic systems. They need ongoing evaluation under real-world load. We implement: offline and online eval harnesses, failure-mode taxonomies, error signature detection, data drift + behaviour drift monitors, feedback loops into training or governance layers.
All of this ensures models remain predictable, auditable, and improvable.
Our Point of View: Models Must Behave, Not Just Perform
LLMs are easy to use but hard to trust.
In real enterprises—banks, insurers, regulators, payments networks, public institutions—the challenge is not generating text.
The challenge is designing models that exhibit:
- domain fidelity, not hallucination
- controlled reasoning, not uncontrolled inference
- measurable behaviour, not guesswork
- traceable provenance, not opaque answers
- safe integration, not accidental escalation paths
We specialise in LLM adaptation where:
Bugni Labs focuses not only on fine-tuning, but full-stack behaviour shaping: data, representations, constraints, context routing, tools, and evaluation.
Where LLM Adaptation Creates Real Value
LLM adaptation produces its highest ROI in domains that require both precision and scale:
Regulatory and compliance workflows
Explainable decisions, evidence extraction, rule/policy interpretation.
Risk & fraud detection orchestration
Models as reasoning layers inside multi-agent detection flows.
Financial & payments platforms
Consistency across KYC/KYB, onboarding, case management, credit decisioning.
Customer operations and service
Stable domain language across channels, fewer escalations, higher automation.
Document-heavy processes
Interpretation, summarisation, structured extraction with grounding to domain meaning.
Event-driven architectures
LLMs acting as decision nodes within distributed workflows.
How We Deliver
Phase 1 — Domain & Behaviour Definition
- →Domain maps
- →Taxonomies
- →Vocabulary alignment
- →Event semantics
- →Risk classification
- →Allowed and prohibited behaviours
Output: A behavioural contract for the model.
Phase 2 — Data Curation & Dataset Engineering
- →Golden datasets
- →Synthetic generation with human correction
- →Edge-case and adversarial data
- →Regulatory data labelling
- →Bias and fairness controls
Output: Datasets that produce models you can trust, not just deploy.
Phase 3 — Adaptation: Fine-tuning + RAG + Tool Integration
- →Fine-tuning (SLM or LLM)
- →Retrieval design
- →Tool/API design
- →Structured output constraints
Output: Models that behave predictably inside real systems.
Phase 4 — Evaluation & Observability
- →Behavioural eval harness
- →Safety / risk tests
- →Context drift detection
- →On-call views for live model behaviour
Output: Models that remain stable, traceable, and governable over time.
What Makes Bugni Labs Different
1. Domain-first, not model-first
We ground everything in domain truth—not parameter counts or vendor roadmaps.
2. AI-native engineering, not demos
We integrate models into complex enterprise systems with real SLAs.
3. Evaluation and observability by design
Monitoring, traceability, and safety are part of the architecture, not bolt-ons.
4. Multi-agent integration expertise
We build systems where models collaborate with agents and runtimes.
5. Strong engineering controls
Versioning, governance, safety layers, approval workflows.
6. Pragmatic, not hype-driven
We don't oversell LLMs. We deliver where they genuinely create strategic leverage.
Example Work
Evidence Extraction & Regulatory Narratives
Extracting claims, obligations, and evidence from complex documents, with structured audit trails.
View case studyCredit Decisioning Reasoning
LLM-augmented reasoning layer for scoring and explainable outputs.
View case studyScreening Enhancement
LLM-powered enrichment for sanctions and PEP screening flows.
View case studyYour domain is complex. Your models should be precise.
If you're working in a regulated, high-stakes, or domain-heavy environment, behaviour-shaped LLMs unlock automation, clarity, and scale with quality preserved.
We help you get there—safely.