Deft GPT partnered with Techling to create a strong AI system that can combine several advanced models into a single platform. This system integrates the latest technologies from OpenAI, Google Pro, Allama Index, and customized cloud-based models, some running on dedicated GPUs and accessed through APIs.
The purpose was to provide a dynamic and intelligent AI assistant to meet user needs, from writing support and data analysis to document parsing and image generation. However, bringing such a versatile ecosystem to life involved several critical challenges.
Imagine getting several individuals who speak different languages to work together on one project. That was the knot in the cord. All the AI models had their own input/output formats, token rules, and structural logic. Getting them to “talk” to one another within one platform was a significant technical challenge.
Every API model (such as OpenAI’s GPT or Google’s PaLM) has a strong token limit. If a user initiated a long conversation or uploaded a large file, these limits would be reached, and there could be failed responses and high costs.
Users required the system to read and reply to diverse document formats—think of the DOCX job descriptions, the SRT transcripts, or spreadsheet invoice information. However, not all models support such formats out of the box.
These issues were overcome by Techling through a full-stack, custom-built backend that made Deft GPT competent and responsive.
Suppose a user is having a long conversation with the AI, and suddenly, the user asks the AI to summarize an entire PDF document. The majority of the systems would crash or interrupt the message.
Techling created a backend checker that initially scans the total message size. If the message is too long, the system smartly deletes the oldest messages (without losing the context of the core conversation) and processes the new input. This led to smooth and meaningful discussions, even in long sessions.
When a user uploads 200 support tickets, ask, “Which ones mention login problems?” The system runs embedding operations to determine the meaning of each document. Techling leveraged serverless models so these tasks could be taken care of immediately and scaled up when heavy use is required without the client worrying about server limitations.
One of the design teams enters a text prompt, “Generate a futuristic city skyline at dusk.” The system uses DALL·E and Diffusion models to produce a high-quality image that it automatically upscales for presentation purposes. This attribute enabled non-designers to produce visual material with much ease.
An HR manager uploads resumes in DOCX format and cover letters in PDF format. The platform extracts data relevant to the platform’s use, evaluates candidate fit, and rewrites sections for tone and clarity. With Techling’s incorporation of smart file processing layers, the system processed DOC, DOCX, PDF, Excel, SRT, and EML formats without problems.
The back end of the system filters and compresses requests to meet token limits while still retaining important context, which avoids errors in large chats.
In multi-step conversations such as code reviews or legal analysis, the system remembers the most important details by managing and refreshing its context memory.
Incorporating voice input makes interaction faster and more interesting, particularly for mobile versions or multitaskers.
With text-to-image generation and scaling, the platform is of value to marketers, educators, designers, etc.
Even if the user requires a relatively simple API chat or complete document analytics, the system can scale accordingly — due to its serverless embedding model architecture.
| Component | Details |
|---|---|
| Cloud Models | Open AI, Google Pro, Allama Index, Custom AI models |
| Deployment | Serverless for embedding tasks; GPU-powered for heavy models |
| Backend | Custom-built API layer with state-of-the-art token implementatio |
| Image Model | DALL·E, Defusion |
| Doc Support | DOC, DOCX, PDF, Excel, SRT, EML |
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