LLM Finetuning Services
Senior engineers, assigned to your build
Go from generic to domain specific. Unlock the full potential of large language models with specialized finetuning that transforms general-purpose AI into domain experts.
Evaluate my fine-tuning case→Why teams pick ABM Tech
Security built in, not bolted on
Encryption, access control and compliance designed into the architecture from the first sprint — the way our Swedish healthcare consent platform was built.
Working hours that overlap
Singapore-based engineers who keep to your business day, so standups, reviews and decisions happen live rather than overnight.
You interview them first
Named engineers put forward with real profiles. You meet anyone joining your engagement before they start, and nobody below the bar gets proposed.
Productive inside two weeks
Scoping, team assembly and access sorted without a procurement marathon — first commit typically lands in week two.
Reviewed in the open
Our delivery record is published and verifiable on Clutch, DesignRush and The Manifest rather than summarised in a slide.
Engagements that keep going
Most clients extend past the first delivery, which is the only retention signal that actually means anything.
LLM Finetuning Services
Fine-tuning a large language model makes sense in a narrow but high-value set of cases: when your domain vocabulary is genuinely out-of-distribution for a general-purpose model, when prompt engineering has hit a quality ceiling you cannot engineer past, or when you need consistent format adherence at latency and cost targets that exclude large frontier models. Outside those conditions, fine-tuning is an expensive distraction — and knowing which case you are actually in is the first thing ABM Tech establishes.
When fine-tuning is the right answer, the outcome depends almost entirely on dataset quality. Our ML engineers have built proprietary data pipelines for synthetic data generation, deduplication, and quality filtering across industries where labeled examples are scarce — from clinical notes to logistics exception reports to legal contract clauses. A model fine-tuned on 2,000 carefully curated examples routinely outperforms one trained on 50,000 noisy ones.
ABM Tech has been doing custom model work since before the term 'fine-tuning' entered mainstream product vocabulary. That depth means we can navigate the full decision surface: base model selection, supervised fine-tuning versus RLHF versus DPO, LoRA and QLoRA for cost-efficient adaptation, serving infrastructure, and the regression testing that ensures your fine-tuned model does not silently degrade on capabilities your users depend on.
The challenge
Teams reach for fine-tuning too early — burning months of engineering time and significant compute budget on a technique that better prompt engineering or retrieval augmentation would have solved in a week. Conversely, teams that genuinely need fine-tuning often attempt it without the data infrastructure to get signal from the process, producing models that are worse than the base model on held-out examples.
Our approach
ABM Tech begins every fine-tuning engagement with a diagnostic sprint: we baseline your current approach with rigorous evals, identify where it fails, and determine whether fine-tuning is actually the right lever. When it is, we build the data pipeline first — curation, filtering, synthetic augmentation — then select the adaptation method (SFT, DPO, LoRA) against your serving constraints, train on managed infrastructure, and run a full regression eval before any model touches production traffic.
The outcome
A completed fine-tuning engagement delivers a versioned, regression-tested model artifact, a reproducible training pipeline you can retrain when your domain data grows, a serving setup with cost-per-request instrumentation, and clear documentation of where the fine-tuned model outperforms the base and where it does not — because understanding the boundaries is as important as the gains.
We'll tell you in 2 weeks whether fine-tuning is the right lever — and what it will cost.
Case Studies
Built, launched, and still running.
Selected engagements
Browse all cases→
AI & Data Science
Sentiment Analysis & Trend Prediction
Multilingual NLP pipeline reading sentiment, sarcasm and emerging trends across global social channels.
Consent Management System Integration
Healthcare · Sweden
Digital patient consent platform for Sweden's healthcare sector — BankID-integrated, cutting consent processing time by 86%.
Online Consultation Platform Integration
Healthcare · Telehealth
HIPAA-compliant telehealth platform pairing people with qualified therapists — booking, secure video sessions and progress tracking.
Shopping Platform Creation
E-commerce
Deal and coupon aggregation platform with algorithmic coupon stacking across leading U.S. retailers.
Integrating APIs with the ServiceNow Platform
Enterprise Integration
REST API integration into ServiceNow that replaced error-prone manual workflows with automated ones.
The metrics that follow from shipping with senior engineers
4.9 / 5
Average client rating across platforms
93%
Net Promoter Score
Long-run
Client retention rate
Secure
Type II certified
Pick the engagement that fits
Four ways to work with us — from surgical staff augmentation to fully managed delivery. All models share the same senior-first talent bench.
Dedicated Teams
Full-time engineers embedded in your team for long-running engagements.
Explore Dedicated Teams↗Staff Augmentation
Add senior specialists to an existing team — vetted, onboarded, and up to speed in weeks.
Explore Staff Augmentation↗Project Delivery
Managed fixed-scope projects with a committed timeline and deliverables.
Explore Project Delivery↗Why ABM Tech
What keeps clients past the first delivery.
What clients tell us made the difference, usually somewhere around the second sprint.
Data Pipeline Before Training
We build the curation, deduplication, and quality-filtering pipeline before a single training run — because dataset quality determines 80% of fine-tuning outcomes.
Right Adaptation Method
SFT, DPO, LoRA, QLoRA — we select the adaptation technique against your accuracy targets, serving latency budget, and hardware constraints rather than defaulting to the most-hyped approach.
Rigorous Before/After Evals
Every fine-tuned model is validated against a held-out benchmark specific to your use case. We report where it improves, where it regresses, and what trade-offs you are accepting.
Cost-Efficient Serving
LoRA and QLoRA adapters let you run fine-tuned capability on smaller, cheaper base models — often delivering meaningful inference cost savings versus frontier API pricing when quality on your specific task is comparable.
Reproducible Retraining Pipelines
We deliver a versioned training pipeline so your team can retrain as domain data accumulates, without starting from scratch or depending on ABM Tech for every model update.
Data Residency and IP Protection
Fine-tuning on sensitive domain data can be run entirely within your cloud account — no proprietary data leaves your environment, and resulting model weights are yours, not ours.
Why Teams Choose Us
Security built in, not bolted on
Encryption, access control and compliance designed into the architecture from the first sprint — the way our Swedish healthcare consent platform was built.
Working hours that overlap
Singapore-based engineers who keep to your business day, so standups, reviews and decisions happen live rather than overnight.
Top Rated
Near-perfect satisfaction scores across Clutch, DesignRush, and Manifest.
How we work
Scoping call to production release.
Every engagement is staffed with named people whose only assignment is your build. No shared allocation, no roster of contractors matched to a brief.
Week zero
We start by arguing with the brief.
A working session on the problem, not a requirements hand-off.
The first conversation is technical. We go through the system you have, the constraints you are stuck with, and what would count as this having worked — and we push back where the brief and the goal disagree. The people in the room are the ones who would build it, because nobody else can tell you the architecture will not hold.
A walk through your existing stack, data and integration constraints
Run by the engineers who would staff the build, not an account manager
You leave with a scope, a team shape and the risks named out loud
FAQ
The questions that come up before you start.
Engagement models, pricing, security, and how we staff a project — answered straight, with the detail you would ask for on a first call anyway.
The clearest indicator is a measurable quality gap on a specific, well-defined task that persists after you have invested seriously in few-shot examples and retrieval augmentation. If your task requires consistent output formatting, domain-specific jargon comprehension, or behavior that few-shot prompting cannot reliably produce even with 10+ examples, fine-tuning is worth evaluating. Our diagnostic sprint (typically 1–2 weeks) establishes this baseline before you commit to the full investment.
Keep exploring