Generative AI development company that ships LLMs into production.

Generative AI systems, large language models (LLMs) among them, produce new content - text, code, images, structured data - from patterns learned in training data. Traditional machine learning classifies or predicts against categories that already exist. Generative AI creates novel output, which is what makes it useful and what makes it risky.

The business applications we are asked for most often are content creation and summarisation, conversational customer support, knowledge extraction from unstructured documents, code generation and technical documentation, decision support, and personalised recommendations at scale.

The useful question is not "can we use LLMs". It is "where does a language model create an advantage specific to this business", and answering that is what the discovery conversation is for. PixelForce has shipped 100+ products generating $1.5B+ in combined client revenue, so we can tell the difference between an AI feature that moves a business metric and one that only sounds impressive in a board pack.

The clearest example sits in our own portfolio. We shipped an AI knowledge system for EzLicence, called The Handbook, in 4 weeks. It consolidated seven years of product evolution, it delivers a 50 percent efficiency gain across workflows, and it automates 90 percent of documentation updates with 10 percent human oversight. That is a retrieval-grounded language system doing unglamorous internal work, and it paid for itself quickly. If your interest is broader than language models, our AI-powered app development service covers building AI into a product end to end.

Generative AI and LLM development at PixelForce is priced inside the standard engagement envelope: Phase 1 Scoping and Design typically $35,000 to $65,000, then Phase 2 Development, QA and Release typically $100,000 to $350,000.

PixelForce prices generative AI work inside the same engagement envelope as every other build, because the discipline that keeps an LLM project honest is the same discipline that keeps any product honest: scope it before you cost it.

Phase 1, Scoping & Design, is typically $35,000 to $65,000. It produces the Business Requirements Document, the UX/UI design, the Product Requirements Document and a fixed-cost Statement of Work for the build. For a generative AI project it also produces the things that decide whether the build succeeds at all: the evaluation set, the data inventory, the retrieval design and an honest read on data readiness. No Blueprint, no Build.

Phase 2, Development, QA and Release, is typically $100,000 to $350,000, set by what is actually being built - how many data sources feed retrieval, how much regulated or financial logic sits underneath, whether there is a mobile client, whether fine-tuning is in scope. The $350,000 figure is a recommendation rather than a ceiling. With a larger budget we still advise capping version one near it and spending the remainder on post-launch iteration driven by real usage.

Phase 3, Post Launch Support, has two options. Option 1 is Warranty, Monitoring & Support at $4,000 per month, covering critical-bug warranty, 24/7 infrastructure monitoring, business-hours incident response and a monthly Platform Health Report, with technical support capped at seven hours per month. Option 2 is the Product Retainer, which includes everything in Option 1 and adds a roadmap workshop, continuous sprints and quarterly business reviews, priced per four-week cycle from Steady $10,000 to Momentum $50,000, with Enterprise on application. Generative AI systems benefit from a retainer more than most software does, because prompts, retrieval quality and model versions all drift.

Two costs sit outside that envelope. Model and API usage is billed by the provider you choose and the rates change frequently, so we do not publish them here - check the current rates on the Anthropic, OpenAI or AWS Bedrock pricing pages. Cloud hosting is billed to your own AWS account. Each PixelForce figure quoted here is an envelope shaped by scope, never a fixed quote off a rate card.

Retrieval-augmented generation solves the two problems that stop a raw language model being useful in a business: it does not know your proprietary information, and it will invent a confident answer rather than admit that.

Without RAG, you ask the model to write a support response and it produces something fluent that cannot reference your product documentation, your policies or that customer's history. It may read well and be wrong.

With RAG, the relevant documents are retrieved from your own knowledge base first, usually through vector search over embeddings, and passed to the model as context alongside the question. The model then answers from your material rather than from its training data, and the answer can cite the source it came from. Grounded, checkable, and correctable by editing a document rather than retraining a model.

That last point is why RAG is the default architecture for enterprise language systems rather than an experiment. It is the most practical way to give a model access to proprietary knowledge without training anything, it keeps the knowledge current because updating the source updates the answers, and it reduces hallucination substantially because the model is asked to summarise supplied text rather than recall facts.

EzLicence's Handbook is exactly this pattern in production: seven years of accumulated product knowledge consolidated into a system that delivers a 50 percent efficiency gain across workflows and automates 90 percent of documentation updates with 10 percent human oversight.

Prompt engineering shapes the input so a model produces the output you want, while fine-tuning trains a model on your own examples; PixelForce starts with prompt engineering and fine-tunes only where measured evidence demands it.

Prompt engineering means shaping the input so the model produces the output you want. No training, no data pipeline, no model hosting. It is cheap, it iterates in minutes, and it is more often sufficient than vendors admit.

Prompt engineering is the right first move when you need a frontier model to follow a specific format or tone, when you want to A/B test approaches quickly, when the requirement is still moving, or when you do not yet have domain-specific labelled data.

Fine-tuning means training a model on your own examples so it internalises a task. It earns its cost when you have hundreds or thousands of labelled examples showing the exact output you need, when the domain language is genuinely niche or technical, when you want a smaller and cheaper model to match a larger one on a narrow task, or when consistency of format matters more than breadth of capability.

Our approach is sequential and deliberately unfashionable. Start with prompt engineering. Measure against a real evaluation set. If the results are good, ship. If the results are inconsistent in a way that your examples show the base model consistently missing, then fine-tune. Do not fine-tune speculatively, and do not spend six months on prompt scaffolding when fine-tuning would have solved the problem more reliably. The decision is made against measured evidence in Phase 1, not asserted.

Hallucination - a model confidently generating false information - is the single largest risk in production generative AI. It cannot be eliminated. It can be controlled, and the control is architectural rather than magical.

Ground the model with retrieval. Do not ask the model to know something. Retrieve it from your knowledge base and pass it in as context. If the information is not there, the system says it does not have it instead of inventing an answer.

Score confidence and set thresholds. Not every output deserves equal trust. Below a threshold, the system escalates to a person or returns a safe default rather than passing on a guess.

Validate the output. For anything structured, the response is parsed and checked against business rules before it reaches a user. An output that violates a constraint is logged and retried rather than shipped.

Monitor and feed back. We track where users reject or correct model output, which is what surfaces hallucination in the wild rather than in testing, and that evidence drives prompt changes, retrieval fixes and evaluation cases.

Keep a human in the loop where the stakes justify it. For medical, legal or financial decisions the model recommends and a person approves. That is not a technical failure, it is correct design. The organisations running language models well in production are not trying to make hallucination impossible. They are building systems in which a hallucination cannot do damage.

There is no single best LLM: frontier commercial models from OpenAI and Anthropic are the usual starting point, open-weight models such as Llama and Mistral suit self-hosting, and smaller specialist models can beat both on a narrow task.

There is no single answer, and any development company that gives you one before understanding the problem is selling a preference. We stay technology-agnostic, because the right model is decided by the requirement.

Frontier commercial models from OpenAI and Anthropic are the usual starting point. They are the most capable general-purpose options, they have mature APIs and tooling, and they let you validate a concept without standing up infrastructure. Both vendors publish their own capability and pricing detail, and it changes often enough that we will not restate it here.

Open-weight models such as Llama and Mistral can be self-hosted. That matters when data must not leave your infrastructure, when volume makes per-token API pricing uncomfortable, or when you want to fine-tune and own the result. The trade-off is that you now operate the model: hosting, scaling, updates and evaluation all become yours.

Smaller specialist models are underrated. A compact model fine-tuned on your task can outperform a frontier model on that task, at a fraction of the latency and cost. If either is a hard constraint, it is worth measuring rather than assuming.

In Phase 1 we assess capability required, latency and throughput, cost sensitivity, data privacy obligations and whether custom training is genuinely needed, then recommend accordingly. The usual recommendation is to validate on a commercial API and design the system so the model is a replaceable component. Vendor lock-in in generative AI is mostly self-inflicted: keep prompts, retrieval and evaluation in your own codebase behind an abstraction and swapping models becomes a configuration change rather than a rebuild.

Yes, your data can stay private when using LLMs, and how depends on the deployment: a public API sends prompt content to the provider, self-hosting keeps everything inside your own infrastructure, and most clients land on a hybrid.

Yes, and the honest answer is that it depends on which model and how you use it. This is a design decision made early, not a setting toggled later.

Using a public API sends your prompt content to the provider's servers. Providers publish privacy terms and enterprise agreements that exclude your data from training, and for many businesses and many use cases that is perfectly acceptable. For regulated data, or for material you are contractually forbidden from disclosing, it may not be.

Self-hosting an open-weight model keeps everything inside your own infrastructure, so no prompt ever leaves your systems. You take on the operational cost of running and updating it in exchange.

A hybrid split is what most of our clients land on. Sensitive processing runs on self-hosted models or is redacted before it leaves, while general tasks use a commercial API. That balances capability, cost and exposure instead of forcing a single answer on every workload.

In Phase 1 we work through what actually needs to stay private, what the consequence would be if it did not, your appetite for running infrastructure, and the budget, then recommend the approach that fits all four. Client IP and client data stay on the client's own AWS account, as they do on every PixelForce engagement. Our data privacy glossary entry covers the underlying terminology.

A production generative AI solution at PixelForce typically takes 4 to 8 weeks for Phase 1 Scoping and Design and 3 to 6 months for Phase 2 Development, QA and Release, with data readiness the biggest variable.

The shape of a PixelForce engagement is the same for generative AI as for any other product, and that consistency is deliberate.

Phase 1, Scoping & Design, typically runs 4 to 8 weeks. Workshops, the BRD, the UX/UI design, the PRD and a fixed-cost SoW, plus the generative-AI-specific work of building the evaluation set and auditing whether your data can actually support retrieval.

Phase 2, Development, QA and Release, typically runs 3 to 6 months for a production system, covering ingestion and retrieval, the application and API layer, guardrails and evaluation harness, monitoring, UAT and release.

Two real examples set the range better than an estimate does. EzLicence's Handbook, a focused internal AI knowledge system, shipped in 4 weeks. OpBill's AI-powered OCR claiming flow, a full consumer-facing product, was built in 4 months and made medical billing 90 percent faster with 98 percent user satisfaction.

What moves the timeline most is data readiness. Clean, accessible, reasonably structured source material compresses the schedule. Content scattered across legacy systems, or locked in scanned documents, makes data preparation the critical path, and no amount of engineering enthusiasm changes that. We assess data readiness during Scoping & Design precisely so the timeline you are given is one that holds. If the point is to test an idea rather than launch a product, our AI MVP and rapid prototyping service is the faster and cheaper route in.

RAG and fine-tuning solve different problems and are frequently combined: RAG changes what the model knows, fine-tuning changes how the model behaves, and treating fine-tuning as a way to load facts is an expensive mistake.

They solve different problems and are frequently combined, so the framing to avoid is treating them as competing products.

RAG changes what the model knows. Use it when the answer depends on your information - documentation, policies, catalogues, tickets, contracts - and especially when that information changes. Update a document and the next answer is current, with no retraining. RAG also makes citation possible, which is usually what turns an interesting demo into something a compliance team will approve.

Fine-tuning changes how the model behaves. Use it when the requirement is consistent format, tone, structure or a narrow classification the base model keeps getting slightly wrong, and when you have the labelled examples to teach it. Fine-tuning does not reliably install facts, and treating it as a way to load a knowledge base into a model is the most expensive mistake we see teams make.

In practice a mature system often does both: retrieval supplies the facts, a fine-tuned or carefully prompted model supplies the behaviour. Our default recommendation is to build the retrieval layer and the evaluation harness first, because until you can measure output quality you cannot tell whether fine-tuning improved anything. If the model needs to take actions rather than answer questions, you are describing an agent, and our AI agents and automation service covers that territory.

PixelForce controls the running cost of an LLM in production by designing for it: routing by difficulty, caching aggressively, controlling the retrieval context, instrumenting spend per feature, and setting hard budgets and alerts.

Generative AI is one of the few things we build where the cost curve keeps moving after launch, so cost control is designed in rather than discovered in an invoice.

Route by difficulty. Most requests in a real system are easy. Sending every one of them to the largest available model is the most common source of avoidable spend. We route simple requests to smaller and cheaper models and reserve the frontier model for the cases that need it.

Cache aggressively. Repeated questions, repeated document context and repeated system instructions do not need to be recomputed every time. Response and context caching typically removes a large share of traffic before it reaches a model at all.

Control the context. Retrieval that returns ten documents when three would do is paying for tokens that add nothing and often makes answers worse. Tuning what gets retrieved improves quality and cost together.

Instrument spend per feature. We report model spend against the feature that caused it, so the conversation becomes "this workflow costs this much and saves that much" rather than a single unexplained line item.

Set budgets and alerts. Hard limits, per-tenant quotas and alerting on anomalies stop a loop or an abusive user turning into a bill. Provider rates change frequently, so check the current pricing on the provider's own page and treat any figure quoted elsewhere as out of date.

PixelForce measures whether a generative AI system is working by building the evaluation set before building the feature, then scoring every prompt, retrieval and model change against it and tracking operational signals in production.

By building the evaluation before building the feature. This is the discipline that separates a generative AI system you can operate from one you can only demonstrate.

During Scoping & Design we assemble an evaluation set: real questions or inputs from your business, paired with what a good response looks like. It is unglamorous work, usually a few hundred examples, and it is the single highest-leverage thing a team can do before writing code.

Every change is then measured against that set. A new prompt, a different retrieval strategy, a new model version - each is scored, so improvement is demonstrated rather than asserted. Regression is caught before release rather than reported by a customer.

In production we track the operational signals that actually predict trouble: how often users correct or reject an answer, how often the system declines to answer, how often retrieval returns nothing relevant, latency at the tail, and cost per interaction. Those numbers go into the monthly Platform Health Report on a support or product retainer.

Underneath it all sit the business metrics the project existed to move. For EzLicence that was a 50 percent efficiency gain across workflows and 90 percent of documentation updates automated. If a generative AI system cannot be tied to a number like that, the honest recommendation is usually not to build it.

Yes. PixelForce is an Australian company with headquarters in Adelaide at Level 3, 97 King William Street, Kent Town, and a presence in Sydney. Development is 100 percent in-house from Australia - there is no subcontracting chain between the conversation and the code.

Generative AI work travels well. There is no app store review, no local hardware and no physical delivery, and the collaboration is document-heavy rather than location-dependent, which is why a meaningful share of enquiries for this service come from outside Australia. We have delivered for clients across a range of markets, including SuspectED for Flinders University, which has been deployed in developing countries including India, Pakistan and Indonesia.

The practical considerations are timezone overlap, which we plan into the sprint cadence, and where your data must legally reside, which is an architecture decision made in Phase 1 and implemented in your own AWS account in the region you require. If those two things work for you, distance is not the constraint people expect it to be.

Choose a generative AI development company on whether it can measure its own output. Ask how the evaluation set is built, how many real examples it holds, and how a prompt, retrieval or model change is scored against it before release.

Then test the architecture questions. Ask how hallucination is controlled, and look for retrieval grounding, confidence thresholds, output validation and human oversight sized to the stakes rather than a claim that the model is simply accurate. Ask how your data is kept private, and expect a specific answer about which workloads use a public API, which are self-hosted or redacted before they leave, and why. Ask whether the model sits behind an abstraction as a replaceable component, because vendor lock-in in generative AI is mostly self-inflicted.

Be wary of a company that names its preferred model before it understands your problem, and of any provider quoting model or token rates in its own marketing, since those rates change frequently and belong on the provider's own pricing page.

Finally, ask which business number the system is meant to move. If a generative AI system cannot be tied to one, the honest recommendation is usually not to build it.