Model selection and architecture
We pick the model and provider for your accuracy, latency, and cost targets, and design the integration so you can swap models later without a rewrite.
002LLM/GPT Integration
LLM and GPT integration that ships to production
We connect large language models like GPT and Claude to your product and your data, then build the retrieval, guardrails, and evaluation around them so the feature holds up with real users. This is for CTOs and product leaders who want an AI feature in production, not a demo that falls apart on edge cases.
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What we build
What an LLM integration includes
An LLM feature is more than an API call. We build the full path from your data to a reliable answer, and the tooling to keep it that way.
Retrieval and grounding (RAG)
We connect the model to your documents, databases, and APIs with embedding pipelines and vector search, so answers come from your data instead of the model's guesswork.
Prompt and context engineering
We design, version, and test the prompts and context windows that drive your feature, treating them as code with reviews and regression checks.
Tool use and agent workflows
Where the feature needs to act, not just answer, we wire the model to your internal APIs with function calling and MCP, with strict permissions on what it can touch.
Guardrails and safety
Input filtering, output validation, PII handling, and fallbacks for when the model refuses or gets it wrong. The feature fails safely instead of embarrassing you.
Evaluation and cost control
Automated evals that score answer quality on your own test set, plus token accounting, caching, and rate limits so quality and spend stay measurable as usage grows.
How we work
From use case to production feature
(4)- 1
Scope the use case
We start with what the feature must do, what data it needs, and what a wrong answer costs you. That decides the model, the architecture, and how much validation to build.
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Prototype against real data
We build a working slice on your actual documents and queries within the first weeks, so you judge output quality on your data, not a polished demo.
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Harden and evaluate
We build the eval set, add guardrails, tune retrieval and prompts against measured scores, and load test for latency and cost before anything reaches users.
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Ship and monitor
We deploy into your stack with logging, quality dashboards, and cost alerts, then hand over documentation and train your team to own and extend it.
Why Webisoft
Why teams hire us for LLM work
Plenty of teams can call a model API. The gap shows up in retrieval quality, evaluation, and production engineering, which is where we spend most of our time.
Product engineers, not prompt hobbyists
We are a full-cycle software studio. Your LLM feature gets the same architecture, testing, and code review discipline as the rest of your product.
Provider neutral
We work with OpenAI, Anthropic, and open-weight models, and recommend based on your accuracy, privacy, and cost constraints, not a reseller agreement.
Built for your data constraints
We design for your privacy and compliance requirements, including self-hosted and VPC deployments where customer data cannot leave your infrastructure.
You own everything
Code, prompts, eval sets, and infrastructure live in your repositories from day one. No dependency on us to change a prompt or swap a model.
FAQ
Common questions about LLM integration
(4)- It depends on accuracy needs, data privacy constraints, latency budget, and request volume. The most reliable way to choose is to benchmark two or three candidate models on real test cases from the actual use case early in the project and let the results decide. Building the integration behind an abstraction layer keeps switching costs low, which matters because model quality and pricing change frequently.
- Hallucination is managed through grounding and validation rather than eliminated entirely. Common techniques include restricting the model to answering from retrieved context, requiring citations for factual claims, validating outputs against schemas and business rules, and designing honest fallbacks for questions the system cannot answer. The goal is to measure the error rate on real cases and drive it to an acceptable level for the application.
- Yes. Open-weight models can be deployed on a company's own cloud or on-premise hardware when data cannot leave the environment, which is a common requirement in healthcare, finance, and government. Hybrid architectures are also common, where sensitive requests stay on internal models while general requests use a hosted API. The trade-off is that self-hosting adds infrastructure and operations cost.
- Ongoing cost is driven mainly by token volume, model choice, and how much context each request carries. Costs are usually estimated during scoping and then reduced in production with techniques such as prompt caching, prompt trimming, and routing simpler requests to cheaper models. Per-feature cost tracking is a best practice so the spend per use case stays visible as usage grows.
Where we add value
LLM features that hold up in production
The distance between a ChatGPT demo and a reliable product feature is where most LLM projects stall. Our work covers the unglamorous layers that make the difference: retrieval quality, evaluation, cost control, and guardrails.
Retrieval-augmented generation
Most business LLM features are really retrieval problems: the model is only as good as the context you hand it. We build RAG pipelines with deliberate chunking strategies, hybrid search combining embeddings with keyword matching, and reranking, using pgvector, Pinecone, or your existing search infrastructure. Retrieval quality is measured on your documents, not assumed.Model selection and routing
GPT-4 class models, Claude, and open-weight models like Llama each trade off cost, latency, quality, and data control differently. We benchmark candidates on your actual tasks and often route requests, sending cheap models the easy queries and reserving expensive ones for hard cases. That routing decision alone frequently cuts inference spend by a large margin.Evaluation and testing
You cannot improve what you do not measure, and LLM outputs cannot be tested with assertEquals. We build evaluation suites with golden datasets, LLM-as-judge scoring, and regression checks that run on every prompt or model change. This turns prompt engineering from guesswork into an engineering loop with numbers attached.Guardrails and safety
Production LLM features need defenses against prompt injection, off-topic drift, hallucinated claims, and leakage of data the user should not see. We layer input validation, output filtering, grounding checks against source documents, and strict tool permissions. For customer-facing deployments, escalation paths to humans are part of the design, not an afterthought.Cost and latency engineering
Token costs compound quietly, and slow responses kill adoption. We implement prompt caching, context trimming, response streaming, and semantic caching of repeated queries, and we instrument per-feature cost dashboards so finance never gets surprised. Latency budgets are set per use case, since a support chatbot and a batch document processor have very different tolerances.Agents and tool use
Beyond chat, we build LLM systems that take actions: querying databases, calling internal APIs, drafting records in your CRM or ERP. Tool schemas are designed narrowly with explicit permissions, and multi-step workflows get checkpoints where a human approves before anything irreversible happens. We are candid about where agent autonomy is ready and where it is not.
Our approach
How an LLM integration engagement runs
(4)- 1
Use case qualification
We start by testing whether the use case is actually a good fit for LLMs, since some problems are better solved with search, rules, or classical ML at a fraction of the cost. We define what a good output looks like, what an unacceptable one looks like, and what the failure cost is. Use cases that survive this filter get a scoped prototype plan. - 2
Prototype against real data
Within a few weeks we build a working prototype on your actual documents and data, not synthetic samples, because retrieval and prompt behavior change completely with real inputs. The prototype ships with a small evaluation set so quality is a number, not an opinion. This is the decision point for whether to invest in production hardening. - 3
Production hardening
The prototype becomes a product: authentication, rate limiting, guardrails, monitoring, fallback behavior when the model provider has an outage, and integration into your existing application and permission model. We expand the evaluation suite and wire it into CI so no prompt or model change ships blind. Data handling is locked down to match your compliance requirements. - 4
Launch, measurement, iteration
Rollout starts with a limited user group and explicit success metrics such as deflection rate, task completion, or time saved, measured against a baseline. User feedback and logged failures feed a weekly iteration loop on prompts, retrieval, and UX. We hand over dashboards and playbooks so your team can continue tuning after the engagement ends.
FAQ
Questions buyers ask before an LLM project
(6)- Running costs depend on token volume, model choice, and context size, and they scale with usage in a way traditional software does not. A support assistant handling thousands of conversations a month may cost hundreds to low thousands of dollars in inference, while heavy document processing can cost far more. We model expected volumes before building, design with caching and routing to control spend, and give you per-feature cost dashboards so the bill is never a mystery.
- Yes, with the right setup. Major API providers offer enterprise terms with no training on your data and provide data processing agreements, and options exist for regional hosting through Azure OpenAI, AWS Bedrock, or Google Vertex. For stricter requirements, open-weight models can run entirely inside your infrastructure. The larger practical risk is internal: an LLM feature must respect your existing document permissions so users cannot retrieve content they were never allowed to see, and we design retrieval around that.
- You reduce them structurally, then contain what remains. Grounding answers in retrieved documents, instructing the model to cite sources and refuse when evidence is missing, and validating outputs against the source material all cut hallucination rates substantially. For high-stakes outputs we add a verification pass or route to human review. What we will not do is promise zero hallucinations, and any vendor who does is selling you a problem. The engineering question is what error rate the use case can tolerate and how errors get caught.
- Start with prompting and RAG, because they are cheaper, faster to iterate, and keep knowledge current without retraining. Fine-tuning earns its cost when you need consistent style or format at scale, domain-specific behavior prompting cannot reach, or smaller models to hit latency and cost targets. It does not solve knowledge freshness, since a fine-tuned model still will not know yesterday's data. In practice many production systems combine a fine-tuned small model with RAG, and we benchmark before recommending either.
- This is a real operational risk, since providers retire models on their own schedules and behavior shifts between versions. We architect behind an abstraction layer so swapping providers is a configuration change, and the evaluation suite is the safety net: when a new model version lands, you rerun the evals and see exactly what changed before users do. Teams without evals discover regressions from customer complaints, which is the expensive way.
- Begin with a short scoping engagement where we rank your candidate use cases by feasibility, risk, and business value, then build one prototype on real data. One working prototype teaches your organization more than months of strategy decks, and it produces the cost and quality numbers you need for an investment decision. We deliberately include your engineers in the build so capability transfers in-house rather than staying with us.
