Your industry has language the internet never learned.
We train models on your data, your terminology, and your workflows. Built for your environment, not the general case.
What a fine-tuned model actually delivers.
These are outcomes from engagements we have seen with enterprise clients, where we helped them fine-tune models to their domain and context.
83%
Of hallucinations in enterprise AI traced to models with no grounding in company-specific context
10x
More support escalations when a general model handles domain queries vs a fine-tuned one
73%
Of enterprises underestimate AI infrastructure costs, causing pilots to exceed budget
Model that performs.
We scope to what your situation requires.
Supervised Fine-tuning
We train foundation models on your labelled domain data including internal documents, transcripts, records, and workflows. The result is a model that understands your terminology, your entities, and your context without needing it in every prompt.
Domain Adaptation
For industries where general models consistently fall short, legal, clinical, financial, industrial, we adapt model weights to your corpus. Terminology, tone, compliance constraints, and reasoning patterns are native to the model, not patched in at inference.
Alignment and RLHF
We use reinforcement learning from human feedback (RLHF) to shape how the model responds. Refusal boundaries, output format, risk thresholds, and tone are all configurable. The model behaves according to your standards, not the defaults shipped by the base model provider.
Evaluation and Benchmarking
Every model we deliver is evaluated against benchmarks defined with you before training begins. Accuracy, hallucination rate, latency, and regression against the base model are all measured and reported before the model touches production.
Same question. Different model.
The difference is whether the model knows your world.
Prompt
“A client is asking whether they should shift from fixed to floating rate bonds given the current rate environment. How do we advise them?”
In a rising rate environment, floating rate bonds generally outperform fixed rate bonds because their coupons reset upward as rates increase. If rates are expected to fall, fixed rate bonds lock in higher yields. The right answer depends on the client's duration tolerance, income needs, and view on the rate cycle. You should review their investment policy statement and consider a blended approach if there is uncertainty.
For a client at this risk profile, a full rotation to floating is usually premature at this point in the cycle. Your firm's approach in similar rate environments has been to recommend a partial shift, increasing floating allocation to around 30 to 40 percent while maintaining core fixed exposure for income stability. Frame it around the client's income floor first, then duration adjustment second. That sequencing tends to land better in client conversations and aligns with how your advisers are trained to present rate risk.
What happens after you say yes.
Same sequence every time. Scoped to your data.
Know what you have before committing to training.
Data Audit
We assess your data estate for volume, quality, coverage, and labelling completeness. The audit determines whether fine-tuning is viable, what preparation work is needed, and how long the engagement will take.
The right base model for your task and environment.
Model Selection
We select the base model against your requirements: task type, compute constraints, deployment environment, latency targets, and licensing terms. We are not tied to any provider. The decision is documented and signed off before training begins.
Fine-tuning and behaviour alignment in a single pass.
Training and Alignment
Training runs against your curated dataset. Alignment passes follow to shape model behaviour within your defined boundaries: output format, tone, refusal behaviour, and risk thresholds. Both are done in sequence before any evaluation begins.
Measured against criteria agreed before training started.
Evaluation
The model is tested against domain benchmarks defined with you at the start of the engagement. Accuracy on your task type, hallucination rate, latency, and regression against the base model are all measured and reported. The model does not progress until it clears the agreed thresholds.
What makes this different.
Three things that are true about how we work. No caveats.
Model-agnostic. Always.
We work across Llama, Mistral, Falcon, GPT, Gemini, and proprietary architectures. Our recommendation is based on your requirements, not our partnerships.
We handle the full stack.
Data preparation, training, alignment, evaluation, and deployment are all ours. You get one team, one contract, and one point of accountability.
Post-deployment monitoring.
Model performance drifts as your data changes. We build monitoring into every deployment and flag degradation before it affects production outputs.
Model-agnostic. Always.
We work across Llama, Mistral, Falcon, GPT, Gemini, and proprietary architectures. Our recommendation is based on your requirements, not our partnerships.
We handle the full stack.
Data preparation, training, alignment, evaluation, and deployment are all ours. You get one team, one contract, and one point of accountability.
Post-deployment monitoring.
Model performance drifts as your data changes. We build monitoring into every deployment and flag degradation before it affects production outputs.
Results that hold.
Real engagements. Measurable outcomes. Client details withheld by agreement.
4 hrs
Underwriting decision time
Cut underwriting decision time from 3 days to 4 hours
A Canadian property and casualty insurer used a generic LLM to assist underwriters with risk summaries. The model had no awareness of Canadian regulatory requirements, the insurer's internal risk appetite guidelines, or regional loss history patterns. Amorisoft fine-tuned a model on 8 years of policy, claims, and underwriter decision data to align risk scoring to the firm's actual appetite.
Our underwriters kept overriding the old model because it did not know our risk appetite or how we read Canadian regional exposure. Amorisoft trained a version on 8 years of our own decisions. The override rate went from most decisions to almost none.
Chief Underwriting Officer
Property and Casualty Insurer, Canada
Our underwriters kept overriding the old model because it did not know our risk appetite or how we read Canadian regional exposure. Amorisoft trained a version on 8 years of our own decisions. The override rate went from most decisions to almost none.
Chief Underwriting Officer
Property and Casualty Insurer, Canada
Ready to train AI on your data?
One conversation is enough to know if this makes sense.