ScalaCode builds and deploys custom large language model solutions , fine-tuning on LoRA, QLoRA, DPO, and RLHF; serving on vLLM, Triton, and NVIDIA NIM; eval harnesses on golden datasets; observability pipelines for drift detection , for enterprises across 45+ countries. With 13+ years of model deployment experience, our teams take LLMs from frontier-API experiments to production-grade systems running inside your perimeter, with the cost economics and reliability your finance team can defend.
Whether you need to fine-tune Llama 3.3 on proprietary data, deploy Qwen 3 in an air-gapped environment, build evaluation harnesses for a multi-tenant agent platform, or migrate from OpenAI to a self-hosted Mistral or DeepSeek stack to control unit economics, our LLM engineers architect solutions that move the metrics that matter , accuracy on your domain, inference cost per request, time-to-deploy.
Domain-adapt frontier and open-source LLMs to your data , legal, medical, financial, technical vocabulary; brand voice; internal-policy reasoning. We use parameter-efficient fine-tuning (LoRA, QLoRA, IA3) for cost-effective adaptation and full fine-tuning when the domain shift demands it. Typical outcome: 15 to 35 point quality lift on domain-specific tasks vs base models at <5% of full-fine-tune cost.
For organisations with massive proprietary corpora (10B+ tokens of clean domain text), we lead continued pre-training on a base model , typically Llama 3.3 8B / 70B or Qwen 3 , to bake domain understanding into the weights themselves. Used by clients in pharma, financial services, and legal where retrieval alone isn’t enough.
Align fine-tuned models to human preferences using Direct Preference Optimisation (DPO), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning from AI Feedback (RLAIF). Used to align models with brand voice, regulatory tone, or specific decision-making patterns. Critical for high-stakes generative use cases.
vLLM for high-throughput open-source LLM serving (continuous batching, paged attention, multi-LoRA serving). NVIDIA Triton + TensorRT-LLM for GPU-optimised production deployments. NVIDIA NIM for enterprise-grade microservice serving. SGLang for structured generation. We design serving infrastructure for your latency budget, throughput requirement, and cost constraints.
For multi-LLM stacks (the production default in 2026), we build smart routing layers that pick the right model per request based on complexity, latency budget, sensitivity classification, and cost. GPT-5 for nuanced reasoning, Claude Sonnet 4.6 for long-context analysis, Gemini 2.5 Flash for high-volume cheap calls, fine-tuned open-source for domain-specific deterministic tasks. Smart routing typically delivers 5 to 15× cost advantage versus single-model architectures.
Every LLM we ship is paired with a custom eval use , golden datasets, automated metric tracking, human-in-the-loop validation panels, regression detection. Built on OpenAI Evals, Anthropic eval tooling, LangSmith, Braintrust, or custom frameworks. Without rigorous evaluation, LLM quality silently regresses as prompts and models evolve. We treat the eval use as a first-class production deliverable, not an afterthought.
Production prompt engineering goes far beyond writing clever instructions. We build prompt templates with structured outputs (JSON schema validation), few-shot example selection patterns, chain-of-thought scaffolding, tool-use protocols, and meta-prompting layers. Codified in versioned prompt registries with eval-gated deployment.
Production LLM stacks need traces (every prompt, response, latency, cost, model version), drift detection (output quality regression alerts), token-budget tracking per tenant / workflow / user, and incident response for prompt injection or output-quality incidents. Built on LangSmith, Langfuse, Helicone, Arize Phoenix, OpenTelemetry.
For data-sovereignty, regulated, or air-gapped environments, we deploy open-source LLMs (Llama 3.3, Qwen 3, Mistral, DeepSeek) on customer infrastructure , vLLM / Ollama / Triton / NVIDIA NIM. AWS GovCloud, Azure Government, India MeitY-empanelled regions, customer-owned datacenters, hybrid-cloud topologies all supported.
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OpenAI o-series or Claude Sonnet 4.6 with extended thinking for the hard reasoning step. GPT-4.1 / Gemini 2.5 Flash for fast tool-calls and execution. Splitting these layers delivers 5 to 15× cost advantage vs always-using-reasoning-models architectures while retaining the reasoning quality where it matters.
Production LLM stacks route across multiple models per request. Classifier picks: “this request is high-stakes with regulatory implications → GPT-5; this is high-volume Tier-1 support summarisation → Llama 3.3 fine-tune; this is creative drafting → Claude Opus 4.6.” Routing decisions are evaluated and refined over time.
Every model output is constrained to a JSON schema using OpenAI’s structured outputs feature, Anthropic’s tool-use JSON validation, Outlines, or external validators (Pydantic, Zod). Eliminates the “model returned malformed JSON” failure mode. Required for any LLM output that feeds a downstream system.
1M-token context windows (GPT-5, Claude Sonnet 4.6, Gemini 2.5 Pro) enable workflows that were previously impossible , entire contracts, multi-document research, full codebase analysis. We design retrieval + ranking patterns for context windows in the 100K-1M token range, plus context-summarisation strategies when even 1M isn’t enough.
vLLM and SGLang now support efficient multi-LoRA serving , one base model serves many customer-specific LoRA adapters from a single GPU. Lets multi-tenant SaaS deliver per-tenant fine-tuned behaviour without provisioning a separate model per tenant. 50 to 100× cost reduction vs separate model deployments.
Production-mature in 2026. Smaller draft model proposes tokens; larger model verifies. Delivers 2 to 4× throughput improvement for compatible workloads with no quality regression. Available natively in vLLM, TensorRT-LLM, and NVIDIA NIM.
No prompt change, fine-tune, or model swap reaches production without passing the eval use. Evals run on every PR, on every nightly cron, on every model version bump. Quality gates block deploys automatically. This is the single most important LLM-ops practice.
Need LLM expertise embedded in your own team? We staff senior LLM engineers with 3+ years of production fine-tuning, serving, and evaluation experience.
Before model selection, we map the workload , request volume, peak QPS, latency budget, sensitivity classification, geography, regulatory posture, accuracy requirements, cost ceiling. These constraints , not the latest benchmark scores , determine model selection.
We benchmark candidate models on YOUR data, not generic public benchmarks. Production-realistic test sets, your actual prompt patterns, your output validation criteria. Public benchmarks like MMLU, HellaSwag, GPQA correlate poorly with production performance for most enterprise workloads.
Fine-tune only when it’s the right answer. For most workloads, prompt engineering + RAG + few-shot examples gets you 80% of the lift at 5% of the engineering cost. When fine-tuning IS right, we choose the lightest-weight technique that delivers the lift , LoRA / QLoRA before full fine-tune, DPO before RLHF, supervised fine-tuning before reinforcement learning.
Fine-tuning quality is bounded by data quality. We design annotation guidelines, train labellers, run inter-annotator agreement analysis (Cohen’s kappa, Krippendorff’s alpha), and build golden test sets before any model training begins. The first month of most fine-tuning programs is data work, not model work.
We run training on AWS Trainium / SageMaker, GCP Vertex AI Training, Azure ML, or customer-owned GPU clusters (H100 / H200 / B200 where available). Hyperparameter search via Weights & Biases sweeps. Eval after every training run on the golden set , no model gets to production without passing the eval.
Pick the right serving stack for the model and workload. vLLM with continuous batching for OSS LLMs at high throughput. Triton + TensorRT-LLM for latency-critical workloads. NVIDIA NIM for enterprise teams that want a microservice abstraction. Bedrock / Vertex AI / Azure OpenAI for managed paths with no infrastructure burden.
Most production LLM stacks are hybrid , frontier model for nuanced reasoning, fine-tuned open-source for deterministic high-volume tasks, classical ML for the rest. Routing logic picks per request. Designed for cost economics that scale with volume.
Every production LLM ships with input guardrails (prompt-injection detection, PII redaction), output guardrails (toxicity, hallucination, regulated-content filtering), and audit trails. Built on Llama Guard, OpenAI Moderation, NVIDIA NeMo Guardrails, custom classifiers, and policy engines.
Token usage broken down per tenant / workflow / model. Latency / quality / cost trended over time. Drift alerts when model behaviour shifts outside historical bounds. Without observability, LLM costs balloon silently and quality regresses unnoticed.
New models or fine-tunes ship behind feature flags (LaunchDarkly, Unleash, Statsig). Canary on small traffic percentages. Compare against baseline on quality + cost + latency. Promote to full deployment only when canary metrics meet acceptance criteria.
We treat LLMs as production systems with eval harnesses, observability, drift monitoring, and rollback paths , not as research projects. Most LLM programs we’re called in to fix were demos that got rushed into production without these foundations.
We work with whichever model fits the workload , frontier or open-source , and decouple business logic from model specifics so swapping is a config change, not a rewrite. When a better model lands, you benefit without re-engineering.
Production LLM stacks routinely burn 5 to 10× more than they should because no one designed for cost economics. Smart routing, structured outputs, multi-LoRA serving, speculative decoding, and disciplined prompt design typically cut LLM bills 60 to 80% with no quality regression.
We’ve fine-tuned LLMs for healthcare, legal, financial services, and regulated industries , including the data-curation, annotation-quality, and evaluation rigour that off-the-shelf consultants skip. Our domain-adapted models routinely outperform off-the-shelf APIs by 15 to 35 points on domain tasks.
We’ve shipped LLM stacks to customer datacenters, AWS GovCloud, Azure Government, India MeitY-empanelled regions, and air-gapped environments. Open-source serving infrastructure (vLLM / Triton / NIM) is part of our default toolkit, not a bolt-on.
Model selection, fine-tuning, serving infrastructure, evaluation, observability, governance, and ongoing operations under one roof. No handoffs to a system integrator that loses context.
Custom LLMs for contract review, clause extraction, regulatory change summarisation, case-law research. Often paired with GraphRAG architectures for precedent and clause-relationship reasoning.
LLMs for product description generation at scale, conversational shopping assistants, customer review summarisation, sentiment-tagged catalog enrichment. Often paired with our sentiment analysis solutions for review-driven product insights.
Multi-tenant LLM platforms with per-customer LoRA fine-tuning. Custom in-app copilots. AI-powered admin assistants. Embedded analytical reasoning. We've shipped LLM features for SaaS clients with single-day fine-tune-to-deploy pipelines.
Code-completion models (custom Code Llama, DeepSeek Coder fine-tunes), code-review assistants, internal-documentation generators. Integrated with GitHub Copilot extensions, JetBrains plugins, or custom IDE integrations.
Workload audit, model selection benchmarking on your data, fine-tuning vs prompt-engineering decision, serving architecture proposal, cost projection. Starting at $25k-$50k.
Data curation, annotation, training, evaluation, deployment to production serving infrastructure. Includes eval use setup. Typical price $80k-$300k depending on model size and data complexity.
Custom LLM stack with multi-model serving, smart routing, fine-tuning pipeline, eval harnesses, observability, governance, multi-tenant isolation. Typical for SaaS clients building LLM features as a platform capability.
For organisations with massive proprietary corpora. Continued pre-training of an open-source base model on your domain data. 6 to 12 weeks engagement; price scales with corpus size and target model.
Embedded squad , LLM engineer, ML engineer, MLOps engineer, eval specialist, infrastructure engineer , running with your team for 6+ months.
Post-launch operations: eval re-runs on model updates, prompt drift management, cost optimisation, incident response, new model evaluation as the frontier evolves. SLA-backed.
Llama 3.3 70B fine-tuned on internal regulatory corpus for KYC reasoning. Domain accuracy 71% → 94%. Replaced GPT-4 calls for KYC pipeline; cost per case dropped 89%.
Domain-pretrained Llama 3.3 8B for prior-authorization reasoning (HIPAA-bounded, on-prem). Auth turnaround time 5.1 days → 11 hours. Denial rate dropped 27%.
Multi-LoRA serving for 240 customer-specific fine-tunes on a single base model. GPU footprint cut 95% vs separate-model baseline. Per-customer customisation latency <200ms.
Smart routing across GPT-5 / Claude Sonnet 4.6 / Gemini Flash / Llama fine-tune for product description generation. LLM bill cut 73% with quality scores held within 1 point of always-GPT-5 baseline.
DPO-aligned Mistral Large for contract clause extraction. Output passed senior-paralegal review on 92% of test cases vs 67% for prompt-engineered Claude 4.6 baseline.
Eval use rollout caught 14 prompt regressions in first month that would have shipped to production. Quality stability +18 points on user-facing CSAT scores.
Default to prompt engineering + RAG for the first 80% of LLM workloads. Fine-tuning adds engineering cost (data curation, annotation, training infrastructure, eval harnesses, ongoing model maintenance) that often isn’t justified when prompt + RAG gets you within 5 quality points. Fine-tuning IS the right answer when: (a) you need consistent brand voice or response format that prompts can’t reliably enforce; (b) latency or cost requires a smaller model, and a fine-tuned small model beats a prompt-engineered large model; (c) your domain vocabulary or reasoning patterns are sufficiently different from base-model training data that retrieval alone doesn’t close the gap. Most fine-tuning programs we’re called in to fix should have stayed at prompt + RAG.
Depends on the workload profile. GPT-5 and Claude Sonnet 4.6 offer the strongest reasoning and tool-use reliability , use them for steps where decisions matter and your data can be processed in the cloud. Claude Opus 4.6 wins on long-context analysis (1M tokens) and complex multi-step reasoning. Gemini 2.5 Flash wins on cost for high-volume cheap calls. Open-source (Llama 3.3, Qwen 3, Mistral, DeepSeek) wins when you need on-premises deployment for sovereignty, when transaction volume justifies a smaller fine-tuned model, or when unit cost is a hard constraint. Most production LLM stacks we ship use a hybrid with smart routing across multiple models , not single-model standardisation.
LoRA (Low-Rank Adaptation) trains small adapter matrices on top of frozen base-model weights , typically 0.1 to 1% of full-fine-tune parameters. Fast, cheap, multi-LoRA serving on a single GPU is supported. Right answer for most fine-tuning needs. QLoRA (Quantised LoRA) loads the base model in 4-bit precision while training the LoRA adapters , lets you fine-tune 70B+ models on a single GPU. Right answer when you need bigger models on smaller hardware. Full fine-tuning updates all model weights , best quality lift, dramatically higher cost (10 to 100× LoRA), single model per deployment (no multi-LoRA serving). Right answer when LoRA’s quality lift isn’t enough and you have the data + budget.
Discovery and architecture sprints: $25k-$50k. LoRA / QLoRA fine-tuning engagement (data curation + annotation + training + eval + production deployment): $80k-$300k depending on model size, data complexity, and required eval rigour. Full fine-tuning of frontier-class models: $200k-$1M+ depending on model. Domain-specific continued pre-training: $300k-$1.5M depending on corpus size. Ongoing serving cost depends on volume + model , open-source on vLLM typically lands $0.0001-$0.001 per 1K tokens; OpenAI GPT-5 fine-tunes typically $0.005-$0.02 per 1K tokens. Most fine-tuning programs we’ve shipped pay back within 6 to 14 months on cost-per-call savings vs always-GPT-5 baseline.
Yes. For sovereignty, regulatory, or air-gapped requirements we deploy open-source models (Llama 3.3, Qwen 3, Mistral, DeepSeek) on customer infrastructure using vLLM, Ollama, NVIDIA NIM, or Triton Inference Server. Healthcare PHI workloads, banking SR 11-7 model risk requirements, defence / government / public sector all routinely require this. Hybrid deployments (on-prem for sensitive, cloud frontier for non-sensitive) are increasingly common , we design these from the workload constraints, not vendor preference.
An eval use is a curated set of test cases (golden datasets), automated metrics, and acceptance thresholds that every prompt change, fine-tune, or model swap must pass before reaching production. Without it, LLM quality silently regresses as prompts and models evolve , and you don’t notice until users complain or business metrics drop. We treat the eval use as a first-class production deliverable: built day one, expanded over time, and integrated into CI/CD as a deploy gate. The single highest-ROI engineering practice in LLM development. Most production-quality LLM systems we ship have 200 to 2,000 golden test cases by month six.
Five levers. (1) Smart routing , frontier models for nuanced reasoning, fine-tuned open-source for high-volume deterministic tasks; classifier picks per request. (2) Reasoning-vs-execution split , reasoning models for planning, fast cheap models for execution. (3) Structured outputs , eliminates the “model wrote prose when we needed JSON” retry loop. (4) Multi-LoRA serving , one base model serves many customer-specific adapters from one GPU. (5) Speculative decoding , draft model proposes tokens, larger model verifies; 2 to 4× throughput gain. Combined, these typically cut LLM bills 60 to 80% with no quality regression. Most production LLM stacks burn 5 to 10× more than they should because no one designed for cost economics from the start.
A focused prompt-engineered + RAG production LLM application: 4 to 8 weeks. A LoRA / QLoRA fine-tuning engagement to production deployment: 6 to 12 weeks (most of that is data curation + eval use, not training). Full LLM platform build (multi-model serving + routing + fine-tuning pipeline + eval + observability): 3 to 6 months. Domain-specific continued pre-training: 6 to 12 weeks training + 4 to 6 weeks eval and deployment. Fastest credible timeline to first measurable business outcome on a focused workload: 4 to 5 weeks if data is clean and use case is bounded.
They form a stack. LLM development (this page) is the model layer , selection, fine-tuning, serving, evaluation. RAG development services is the retrieval-grounding layer , pipelines that pipe your enterprise knowledge into model context so reasoning is grounded in real documents. AI agent development is the autonomous-agent architecture , systems that plan, take actions, call tools, and traverse enterprise systems built on LLM foundations. Most real programs need all three. We typically lead with model + serving architecture, layer in RAG for grounding, then build agent orchestration on top.
Defense-in-depth across input and output. (1) Input guardrails , Llama Guard, OpenAI Moderation, custom classifiers detect prompt injection patterns, jailbreak attempts, PII before they reach the model. (2) Structured prompts , clear separation between system instructions and user input prevents many injection patterns. (3) Output guardrails , toxicity detection, regulated-content filtering, hallucination flagging on model output before it reaches users or downstream systems. (4) Tool-call sandboxing , agents that execute code or call sensitive APIs run in sandboxed environments with limited blast radius. (5) Audit logging , every prompt + response logged for incident response and forensic review. (6) Eval coverage , adversarial test cases included in the eval use to catch new injection patterns as they emerge. Built on Llama Guard, OpenAI Moderation API, NVIDIA NeMo Guardrails, Lakera Guard, and custom classifier guardrails per workload.
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